+
+ The project is a recipe recommendation system that allows users to either type a textual query or upload images of food items. Based on the inputs β including user-provided tags and detected ingredients β the application returns the most relevant recipes using semantic search and image classification.
+
+
1.1 NLP Task:
+
+ This project addresses the challenge of improving recipe recommendation systems through advanced semantic search capabilities powered by transformer-based language models.
+ We fine-tune BERT (Bidirectional Encoder Representations from Transformers) to capture domain-specific context and understand nuanced relationships between ingredients and cooking techniques.
+ A subset of 15,000 recipes was preprocessed and structured into sequences categorized by food components (proteins, vegetables, grains, etc.) to optimize BERT input.
+ The model learns contextual embeddings that capture semantic meaning between ingredients and tags. Once trained, we generate embeddings for all recipes and use cosine similarity to retrieve the top-K relevant recipes for a user query.
+
+
1.2 CV Task:
+
+ In parallel, the computer vision component focuses on recognizing food items from images using deep learning.
+ We implemented an image classification pipeline based on EfficientNet-B0, trained to classify four distinct food categories: Onion, Strawberry, Pear, and Tomato.
+ In addition to identifying the type of produce, the model also detects intra-class variations, such as whether the item is whole, halved/hulled, or sliced/cored.
+
+ EfficientNet-B0 was chosen for its small size, pretraining on ImageNet (which includes visually similar classes), and ease of deployment. With minimal fine-tuning, it delivered high accuracy in both produce and variation classification tasks.
+
+ The goal was to evaluate both inter-class and intra-class visual consistency using statistical analysis and CNN-based classification.
+ Since the dataset was manually created, image analysis helped us understand variation across samples, identify noise, and decide on preprocessing techniques and model input parameters.
+
+ Together, both the NLP and CV pipelines form a multimodal system that enables recipe recommendations from either text queries or food images, offering a seamless and intelligent user experience.
+
+
+ """, unsafe_allow_html=True)
+
+
+
+
+ # Introduction
+ st.subheader("2. Introduction")
st.markdown("""
- **NLP Engineering Perspective:**
+ In an increasingly digital culinary world, users often look for personalized recipe recommendations based on either what they have at hand or what they crave. While traditional recipe search engines rely heavily on keyword matching, they fail to understand the deeper semantic context of ingredients, cooking methods, and dietary preferences. Similarly, visual recognition of food items can play a key role in enabling intuitive, image-based search experiences β especially for users unsure of ingredient names or spelling.
+
+ This project aims to build an **end-to-end multimodal recipe recommendation system** that supports both **natural language queries** and **image-based inputs**. Users can either type a textual query such as βhealthy vegetarian saladβ or upload an image of a food item (e.g., pear, onion), and the system will return the most relevant recipes. This is achieved by integrating two advanced deep learning pipelines:
+
+ - An **NLP pipeline** that fine-tunes a BERT model to capture culinary semantics and perform semantic recipe retrieval.
+ - A **CV pipeline** that classifies food items and their variations (e.g., whole, sliced) using EfficientNet-B0.
+
+ The project serves not only as a technical showcase of how language and vision models can be combined for real-world tasks, but also as an **educational exercise** that provided the team with hands-on experience in data preprocessing, model training, evaluation, deployment, and user interface design.
+
+ Ultimately, the system demonstrates how domain-specific adaptation of existing state-of-the-art models can lead to an intelligent and user-friendly solution for everyday tasks like recipe discovery.
+ """)
+
+
+ st.markdown("## 3. CV: Produce Classification Task")
+
+ st.markdown("""
+ For our Produce Classification task, we manually captured images of **tomato, onion, pear, and strawberry**, collecting a total of **12,000 images** β approximately **3,000 per category**.
+ Within each category, we introduced **3 intra-class variations**, with around **1,000 samples per variation**, by photographing the produce in different physical states:
+
+ - **Whole:** The item is uncut and intact (e.g., an entire pear or onion).
+ - **Halved/Hulled:** The item is partially cut β for example, a strawberry with the hull removed or a fruit sliced in half.
+ - **Sliced:** The item is cut into smaller segments or slices, such as onion rings or tomato wedges.
+
+ These variations allow the model to generalize better by learning visual features across different presentations, shapes, and cross-sections of each produce type.
+ """)
+
+
+
+ # 3.1 Data Preprocessing and Sample Images
+ st.markdown("### 3.1 Image Preprocessing and Samples")
+
+ st.markdown("""
+
+
+ As we are using the EfficientNet-B0 model, all images in our dataset are resized to 224Γ224 pixels. This is the standard input size for EfficientNet and ensures compatibility with pre-trained weights as well as efficient GPU usage during training.
+
+ Below are sample resized images from each class (`Onion`, `Strawberry`, `Pear`, `Tomato`), illustrating the preprocessing step before feeding them into the model.
+ For training purposes, the images were normalized by dividing each pixel value by **255**.
+
+
+ """, unsafe_allow_html=True)
+
+ col1, _ = st.columns([1, 2]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part1_image_sample.png", caption="Sample 224Γ224 images from each class", use_container_width=True)
+
+
+
+ # 3.2 RGB Histogram Analysis
+ st.markdown("### 3.2 RGB Histogram Analysis: What It Tells Us About the Dataset")
+
+ st.markdown("""
+
+
+ This RGB histogram plot shows the distribution of pixel intensities for the Red, Green, and Blue channels in one sample image per class. Itβs a visual summary of color composition and can reveal important patterns about your dataset.
+
+
+ """, unsafe_allow_html=True)
+
+ st.image("assets/images/part1_image_histogram.png", caption="RGB histogram distribution for one image per class", use_container_width=True)
+
+ st.markdown("""
+ From the above histograms, we observe the following:
+
+ - **Color Signatures:** Each class has distinct RGB patterns.
+ For example, Tomato shows strong red peaks; Pear has dominant green and blue.
+ - **Image Quality:** Irregular or flat histograms may indicate over/underexposed or noisy images.
+ - **Channel Balance:** Most classes show good **RGB variation**, so retaining **all 3 channels** is important.
+ Onions show similar trends across **R, G, B** channels but still contain subtle distinguishing features.
+
+ Based on per-class RGB histograms, we observe the following:
+ """, unsafe_allow_html=True)
+
+ col1, col2 = st.columns(2)
+
+ with col1:
+ st.markdown("""
+ **1. Onion**
+ - Red & Green: Sharp peaks at 140β150
+ - Blue: Broad peak around 100
+ - Likely reflects white/yellow onion layers with soft shadows; blue may be from background or lighting
+ - The model may learn to detect mid-range blue with red-green spikes
+ """)
+
+ with col2:
+ st.markdown("""
+ **2. Strawberry**
+ - Red: Two strong peaks around 80 and 220
+ - Green & Blue: Broader, less prominent
+ - Indicates dominant red intensity typical of strawberries; low blue supports lack of cool tones
+ - The model can distinguish this class easily due to its strong color separation
+ """)
+
+ col3, col4 = st.columns(2)
+
+ with col3:
+ st.markdown("""
+ **3. Pear**
+ - Green & Blue: Peaks between 50β120
+ - Red: Moderate and spread around 100β150
+ - Suggests soft green/yellow pear tones with consistent lighting
+ - Minimal intra-class variation makes this class stable for classification
+ """)
+
+ with col4:
+ st.markdown("""
+ **4. Tomato**
+ - Red: Very sharp peak around 120
+ - Green & Blue: Low and drop off quickly
+ - Strongly saturated red, characteristic of ripe tomatoes
+ - Easy for the model to detect, but caution is needed to avoid overfitting to red alone
+ """)
+
+
+ # 3.3 Average Image Analysis
+ st.markdown("### 3.3 Dataset Analysis Based on Average Images")
+
+ st.markdown("""
+
+
+ The average images of Onion, Strawberry, Pear, and Tomato offer valuable insights into the characteristics of the dataset they were generated from. These images are created by averaging pixel values across all images in each class.
+
+
+
+ #### General Observations
+
+ 1. **Blurriness of All Average Images**
+ - High blur indicates significant variation in object position, orientation, and size.
+ - No consistent alignment or cropping β objects appear in different parts of the frame.
+
+ 2. **Centered Color Blobs**
+ - Each average image displays a dominant center color:
+ - Onion: pale pinkish-grey
+ - Strawberry: red core
+ - Pear: yellow-green diffuse
+ - Tomato: reddish-orange with brown-green
+ - This suggests most objects are roughly centered.
+ Pear and tomato are more **localized and distinct**, while onion and strawberry show more **variation and blur**.
+
+ 3. **Background Color and Texture**
+ - All classes share a gray-brown tone due to a mix of background elements. As multiple colors blend, they tend to shift toward a darker gray.
+ - This suggests the use of natural or neutral settings with a variety of background textures and colors.
+
+
+
+ #### Implications for Model Training
+
+ - Color is a Strong Signal: Average images retain dominant color, confirming the importance of **RGB input**.
+ - Centering Helps: Consistent object centering allows CNNs to leverage spatial regularities.
+
+ """, unsafe_allow_html=True)
+
+
+
+ # 3.3 Average Image Analysis
+ st.markdown("### 3.4 Training and Results")
+
+ st.markdown("""
+ We used a dataset of **12,000 manually labeled images** covering four classes: tomato, onion, pear, and strawberry.
+ The dataset was split in a **50:25:25 ratio** for training, validation, and testing, respectively:
+
+ - **Training:** 6,000 images
+ - **Validation:** 3,000 images
+ - **Testing:** 3,000 images
+
+ Although the typical split is 70:15:15, we opted to test on more data to better evaluate generalization and avoid overfitting.
+
+ Due to hardware constraints (GPU memory limits), we used a **batch size of 32**. We employed the **Adam optimizer** with a learning rate of **0.0001**.
+ We also implemented **early stopping** with a patience of 5 epochs β meaning training stops if no improvement is seen in validation accuracy for 5 consecutive epochs.
+ """)
+
+ # Insert training & validation graph
+ col1, col2 = st.columns([2.75,1.35]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part1_train_validation_graph.png", caption="Training vs Validation Loss and Accuracy", use_container_width=True)
+ with col2:
+ st.image("assets/images/part1_confusion_matrix.png", caption="Confusion Matrix on Test Set", use_container_width=True)
+
+ st.markdown("""
+ The model achieved over **95% accuracy within just the first epoch**.
+ This rapid convergence is primarily due to the use of **EfficientNet-B0**, which is pretrained on ImageNet and already contains low-level visual features.
+ Thanks to **transfer learning**, the model was able to learn quickly on our dataset with minimal training from scratch.
+
+ The model reached peak performance at **Epoch 6**:
+ - **Train Loss:** 0.0178
+ - **Train Accuracy:** 99.46%
+ - **Validation Accuracy:** 99.74%
+
+ The final **test accuracy** was **99.44%**, indicating excellent generalization to unseen data.
+
+ From the confusion matrix, it is evident that the model demonstrates strong **class separability** and **robust generalization**, with only **17 total misclassifications out of 3,035 test samples**.
+
+ This confirms that the model is capable of distinguishing even visually similar classes with high precision.
+ """)
+
+ st.markdown("""
+ #### False Positives / False Negatives (Examples)
+
+ By analyzing the images that were **falsely classified** (false positives and false negatives), we can pinpoint exactly where the model is making mistakes.
+ These examples help us identify whether misclassifications are due to:
+
+ - Visually ambiguous or difficult samples
+ - Blurry or out-of-focus images
+ - Outliers that differ significantly from the training distribution
+
+ Reviewing these cases allows us to better understand the model's true performance and its limitations in real-world scenarios.
+ """)
+
+ col1, col2 = st.columns(2)
+
+ with col1:
+ st.image("assets/images/part1_fn_straw.png", caption="FP/FN for Strawberry", use_container_width=True)
+
+ with col2:
+ st.image("assets/images/part1_fn_onion.png", caption="FP/FN for Onion", use_container_width=True)
+
+ st.markdown("""
+ From the misclassified images, we can deduce that the model struggled **slightly** with images that were **out of focus**, captured in **very dim lighting**, or showed only a **small visible portion** of the object. These conditions made it difficult for the model to accurately identify the class.
+
+ Most misclassifications occurred between **strawberry** and **onion**. These classes exhibited greater variation in object positioning. In some cases, the objects (onion or strawberry) were **partially hidden**, with only a small portion visible, and were also affected by **poor lighting conditions**. Such combinations made it challenging for the model to make accurate predictions.
+
+ However, with an F1-score of **99%** for these classes, we can confidently conclude that the model performed well overall β especially on images where the object was **clearly visible**, **fully within the frame**, and in **good general condition**. This further suggests that the model is **robust and ready for real-world use**.
+
+ Notably, we did not observe any misclassifications for **pear** and **tomato**. Based on our earlier data analysis, images in these classes were generally **well-centered and localized**, which likely contributed to the model's high accuracy (100%) in those categories.
+ """)
+
+ st.markdown("""
+ """, unsafe_allow_html=True)
+
+
+ st.markdown("""
+ #### Learned Feature Maps (Pattern) Analysis
+
+ To understand what our model has actually learned and how it perceives different food items internally, we visualized **feature maps** extracted from various convolutional layers of **EfficientNet-B0**.
+
+ The image below shows a **single most-activated channel per layer** for each class: Onion, Pear, Tomato, and Strawberry β across **9 convolutional stages**.
+
+ """)
+
+ st.image("assets/images/part1_channel_map.png", caption="Feature maps across Conv layers for each class (EfficientNet-B0)", use_container_width=True)
+
+ st.markdown("""
+
+ Each row corresponds to a different class and shows the evolution of feature extraction from **Conv1 to Conv9**, i.e., from shallow to deep layers.
+
+ 1. **Early Layers (Conv 1β3):**
+ - Focus primarily on **edges, textures, and object contours**.
+ - All classes exhibit relatively **fine-grained spatial detail** at this stage.
+ - You can still visually recognize the object (e.g., the onion's round boundary or the pearβs contour).
+ - These layers act like **edge detectors** or **low-level texture filters**.
+
+ 2. **Middle Layers (Conv 4β6):**
+ - Begin to extract more **abstract, localized patterns**.
+ - Object boundaries start to blur, and **high-frequency detail reduces**.
+ - Certain class-specific structures emerge (e.g., the tomatoβs highlight region or the strawberryβs bright patch).
+ - The model starts focusing on **regions of high semantic importance**.
+
+ 3. **Deep Layers (Conv 7β9):**
+ - Feature maps become **coarser and more focused**, losing most spatial resolution.
+ - The network now highlights only **key discriminative regions** β often the **center mass** of the object.
+ - While the original shape is nearly lost, **strong activation in a focused area** indicates high confidence in classification.
+ - This shows the model is no longer looking at superficial textures, but **has learned what features truly define each class**.
+
+ **Key Takeaways:**
+ - Model **successfully learns hierarchical features**: from edges and textures to class-specific abstractions.
+ - The model appears to **localize the object region** consistently across all classes. Especially clear in later layers.
+ - This visualization confirms that the model isnβt just memorizing images but is actually learning **robust visual representations** across depth.
+ """)
+
+
+
+
+
+
+
+
+
+ st.markdown("## 4. Produce Variation Classification Task")
+
+ st.markdown("""
+ As mentioned earlier, we have **3,000 images per class**, and within each class, there are **1,000 images per variation** of **whole**, **halved/hulled**, and **sliced/cored**.
+
+ These variations not only help make our main classification model more **robust to presentation differences**, but also allow us to analyze how the model performs under **intra-class variation** β that is, variation within the same object category.
+
+ ### Importance of Intra-Class Variation analysis:
+
+ - In real-world settings (e.g., cooking, grocery shelves, or user-uploaded photos), food items can appear in multiple forms β whole, cut, or partially visible.
+ - A model that performs well only on whole items may fail when the object is sliced or obscured.
+ - By training and evaluating a separate **variation classifier**, we can:
+ - Assess the **distinctiveness** of each variation within a class.
+ - Understand whether certain variations (e.g., "sliced onion") are harder to distinguish than others.
+ - Identify **confusing cases**, which may need augmentation, re-labeling, or more data.
+ - Ensure that the main classifier isn't biased toward one specific presentation.
+
+ In the following section, we train a dedicated CNN to classify the **variation type** within each produce category, and evaluate its performance across the three variation classes.
+ """)
+
+
+
+
+ # 4.1 Data Preprocessing and Sample Images
+ st.markdown("### 4.1 Image Preprocessing and Samples")
+
+ st.markdown("""
+
+
+ As we are using the **EfficientNet-B0** model, all images in our dataset are resized to 224Γ224 pixels. This is the standard input size for EfficientNet and ensures compatibility with pre-trained weights, as well as efficient GPU utilization during training.
+
+ Below are sample resized images for each class β onion, pear, strawberry, and tomato β showing their intra-class variations: whole, halved/hulled, and sliced.
+ These samples provide a visual sense of the input data and the diversity of presentation styles within each category.
+
+ For training purposes, the images were normalized by dividing each pixel value by 255.
+
+
+ """, unsafe_allow_html=True)
+
+
+ col1, col2, col3, col4 = st.columns([1, 1, 1, 1]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part2_onion_sample.png", caption="Sample 224Γ224 images from **onion** class", use_container_width=True)
+
+ with col2:
+ st.image("assets/images/part2_pear_sample.png", caption="Sample 224Γ224 images from **pear** class", use_container_width=True)
- This project addresses the challenge of improving recipe recommendation systems through
- advanced semantic search capabilities using transformer-based language models. This will explain how to fine-tune a model
- to learn domain-specific context to capture the nuanced relationships between
- ingredients and cooking techniques in culinary contexts.
- Our approach leverages BERT (Bidirectional Encoder Representations from Transformers)
- fine-tuning on a custom recipe dataset to develop a semantic understanding of culinary content.
- We preprocessed and structured a subset of 15,000 recipes into standardized sequences organized
- by food categories (proteins, vegetables, legumes, etc.) to create training data optimized for
- the BERT architecture.
- The model was fine-tuned to learn contextual embeddings that capture semantic relationships
- between ingredients and tags. At the end, we generate embeddings for all recipes in our
- dataset and perform cosine-similarity retrieval to produce the top-K most relevant recipes
- for a user query.
+ with col3:
+ st.image("assets/images/part2_tomato_sample.png", caption="Sample 224Γ224 images from **tomato** class", use_container_width=True)
+
+ with col4:
+ st.image("assets/images/part2_strawberry_sample.png", caption="Sample 224Γ224 images from **strawberry** class", use_container_width=True)
+
+
+ # 3.2 RGB Histogram Analysis
+ st.markdown("### 4.2 RGB Histogram Analysis: What It Tells Us About the Dataset")
+
+ st.markdown("""
+
+
+ This RGB histogram plot shows the distribution of pixel intensities for the Red, Green, and Blue channels in one sample image per class. Itβs a visual summary of color composition and can reveal important patterns about your dataset.
+
+
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ ##### **RGB Histogram Analysis: Onion (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_rgb_hist_onion.png", caption="RGB histogram distribution of onion variation", use_container_width=True)
+
+ st.markdown("""
+ The plots below represent RGB intensity distributions for each of the three onion variations: **Halved**, **Sliced**, and **Whole**. Each line shows pixel frequency across Red, Green, and Blue channels.
+
+ **1. Halved**
+ - **Blue channel** dominates early pixel ranges (peaks ~40β60), suggesting a bluish tint in onion layers or reflections.
+ - Red and Green are moderately aligned, indicating consistent lighting.
+ - Minor peaks at higher pixel values may result from reflective areas or background variance.
+ - **Interpretation:** Halved onions show strong consistency with a subtle blue tone, likely taken in well-lit but slightly cool environments.
+
+ **2. Sliced**
+ - All three channels peak around pixel values 130β150.
+ - Histogram is **smoother and more centered**, indicating balanced exposure and color.
+ - Slight red dominance in the mid-range may be due to the red/pink inner rings being more exposed.
+ - **Interpretation:** Sliced onions offer the most uniform and balanced appearance across all channels β useful for training stability.
+
+ **3. Whole**
+ - Shows **high red peaks** near pixel value 220 and strong green variation around 120β150.
+ - Blue is less dominant and shows more fluctuation in the mid-range.
+ - Histogram is noisier with more channel separation β likely due to outer skin, glare, or inconsistent lighting.
+ - **Interpretation:** Whole onions are visually more complex, capturing skins, glare, and full curvature. This leads to higher variation.
+ - To capture this complexity effectively, using **RGB channels** is essential.
+
+ **Dataset Insights**
+
+ - **Lighting & Background Consistency:**
+ - Sliced and halved images appear more controlled and evenly lit.
+ - Whole images show more **color imbalance and variation**, indicating diverse capture settings.
+
+ - **Model Implications:**
+ - The model may learn **more stable features** from sliced and halved images.
+ - Whole onions may require the model to rely more on **texture and shape** than color.
""")
+
- # Introduction
- st.subheader("Introduction")
+
+ st.markdown("""
+ ##### **RGB Histogram Analysis: Pear (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_rgb_hist_pear.png", caption="RGB histogram distribution of Pear variation", use_container_width=True)
+
+ st.markdown("""
+ The plots below represent RGB intensity distributions for each of the three pear variations: **Halved**, **Sliced**, and **Whole**. Each line shows pixel frequency across Red, Green, and Blue channels.
+
+ **1. Halved**
+ - Displays a **balanced and smooth distribution** across all three channels.
+ - Red, green, and blue channels peak around 130β150, indicating moderate brightness and natural coloration.
+ - No single channel dominates, which suggests good **white balance** and consistent lighting.
+ - **Interpretation:** Halved pears are well-exposed, and color is evenly distributed, making this variation visually clean and useful for training.
+
+ **2. Sliced**
+ - Shows a **strong blue peak at pixel value 0**, indicating the presence of **underexposed or shadowed regions**.
+ - Green and red are more balanced but spread across mid to high intensity values (~50β180).
+ - The histogram shape is more **jagged and variable**, which may suggest inconsistent lighting.
+ - **Interpretation:** Sliced pears may suffer from **lighting inconsistencies**, contributing to visual noise.
+
+ **3. Whole**
+ - RGB curves are tightly packed and peak sharply around **pixel values 80β100**, with a quick drop-off after.
+ - Very little spread across intensity range. Images likely have **uniform lighting** with soft shadows.
+ - Red channel slightly dominates.
+ - **Interpretation:** Whole pears appear **low in contrast and brightness**, which may simplify the learning task.
+
+
+ **Dataset Insights**
+ - **Lighting Conditions:**
+ - Halved images show the best exposure balance.
+ - Sliced images include darker regions, hinting at variability in data quality.
+ - Whole pears are consistently lit but may be low contrast.
+
+ - **Model Implications:**
+ - Halved pears are optimal for training due to stable exposure.
+ - Whole pears may be easily classified due to consistent appearance but may lack variation needed for generalization.
+ """)
+
+ st.markdown("""
+ ##### **RGB Histogram Analysis: Strawberry (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_rgb_hist_strawberry.png", caption="RGB histogram distribution of Strawberry variation", use_container_width=True)
+
+
+ st.markdown("""
+ The plots below illustrate the pixel intensity distributions for Red, Green, and Blue channels across three strawberry variations: **Hulled**, **Sliced**, and **Whole**. These histograms help us understand how light, color, and structure vary within the same class.
+
+ **1. Hulled**
+ - All three channels show strong, distinct peaks around **200β240**, especially red and blue, indicating the presence of **high saturation and bright highlights**.
+ - Moderate peaks across mid-range values (50β150) suggest good contrast.
+ - The histogram is **visually rich**, covering a wide range of pixel intensities.
+ - **Interpretation:** Hulled strawberries are well-lit and contain diverse color information, especially in the red spectrum. Good candidate for training due to high color contrast.
+
+ **2. Sliced**
+ - Displays a **strong green peak near 140** and red around 130β150, which are consistent with the **flesh and seedy outer layer** of strawberries.
+ - Blue is subdued across the entire range, which is expected for strawberries.
+ - Histogram is **tighter and more concentrated** than hulled, with fewer highlights and shadows.
+ - **Interpretation:** Sliced strawberries appear more uniform and less reflective, providing a **clean but slightly less diverse color profile than hulled**.
+
+ **3. Whole**
+ - Broad red and green peaks from **100β160**, with visible spikes around **140β150**, typical of a fully intact strawberry's surface.
+ - Blue is again low, suggesting **minimal background influence** or blue-toned lighting.
+ - Histogram is less spiky and more spread out than others, implying a **more natural condition**.
+ - **Interpretation:** Whole strawberries have balanced exposure and represent general real-world conditions well. Their diverse yet smooth histogram supports good generalization.
+
+ **Dataset Insights**
+
+ - **Lighting & Surface Reflection:**
+ - Hulled strawberries reflect the most light, they show strong bright peaks.
+ - Sliced variations are more **internal-texture dominant**, with reduced highlight intensity.
+ - Whole samples offer the most **balanced histogram**, likely reflecting more consistent and natural lighting.
+
+ - **Model Implications:**
+ - Each variation presents unique spectral patterns, confirming that the model can learn these differences and perform accurate classification.
+ - Their differing RGB distributions also reduce the chance of model overfitting to any single presentation style.
+ """)
+
+
+ st.markdown("""
+ ##### **RGB Histogram Analysis: Tomato (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_rgb_hist_tomato.png", caption="RGB histogram distribution of Tomato variation", use_container_width=True)
+
+
+ st.markdown("""
+ The plots below represent RGB intensity distributions for the three tomato variations: **Diced**, **Vines**, and **Whole**. These histograms reveal how color composition and exposure vary across presentation styles.
+
+ **1. Diced**
+ - The histogram shows **sharp, narrow peaks** for all three channels near pixel values **230β250**, indicating **high saturation and brightness** β possibly due to light reflection from diced surfaces.
+ - A significant spike in the **blue channel at pixel 0** suggests underexposed or shadowed areas, likely from the background.
+ - Minimal spread across the mid-tone range (50β200) implies **low color diversity**.
+ - **Interpretation:** Diced tomatoes contain bright highlight, with limited mid and low-tone information. This variation could confuse the model in **inconsistent lighting** , but it wil performs well under controlled lighting. It also suggests lower variation across images.
+
+ **2. Vines**
+ - Displays a **broad, balanced distribution** across all channels, especially strong in the blue and green spectrum (~20β150).
+ - No strong spikes, suggesting **natural, diffuse lighting** and less glossiness.
+ - Color spread across all pixel values shows **greater background diversity**, possibly due to the inclusion of leaves, stems, or soil.
+ - **Interpretation:** Vines are visually complex and rich in texture, offering the **highest visual diversity** among the three. These images reflect realistic environments.
+
+ **3. Whole**
+ - Strong **red peak near 150β160** represents the core tomato surface.
+ - Green and blue show defined peaks around 90β130, suggesting presence of both background and stem/leaf regions.
+ - Well-defined, multi-peak structure shows moderate saturation and **good contrast**.
+ - **Interpretation:** Whole tomatoes appear cleanly illuminated and well-captured, with a **balanced mix of object and background**. Likely it will be stable and reliable variation for model training.
+
+ **Dataset Insights**
+
+ - **Lighting & Background:**
+ - Diced tomatoes show high extremes of highlights, likely affected by direct light.
+ - Vines exhibit diffuse lighting but introduce **non-tomato color features**.
+ - Whole images appear most balanced and consistent in lighting and color spread but lack high brigntness data.
+
+ - **Model Implications:**
+ - Each variation brings complementary features: diced emphasizes color intensity, vines offer real-world complexity, and whole provides consistency.
+ - Lack of variation may hinder generalization under challenging conditions, but it can perform well in good lighting.
+ """)
+
+
+ # 4.3 Average Image Analysis
+ st.markdown("### 4.3 Dataset Analysis Based on Average Images")
+
+ st.markdown("""
+
+
+ The average images of intra calass variations offer valuable insights into the characteristics of the dataset they were generated from. These images are created by averaging pixel values across all images in each class.
+
+
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ ##### **Average Image Analysis: Onion (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_avg_onion.png", caption="Average image Onion variations", use_container_width=True)
+
+
+ st.markdown("""
+ **Visual Observations**
+
+ 1. **Halved**
+ - Very high blur, suggesting **large variation** in object orientation and placement.
+ - Likely a mix of different halves (top/bottom) with varied alignment.
+
+ 2. **Sliced**
+ - Slightly more blurr than halved, might be cauce of resaon as sliced onion rings takes a very less space in image.
+ - Faint radial patterns hint at partial consistency in shape, which model can learn.
+
+ 3. **Whole**
+ - Most distinct shape and color among the three.
+ - Central reddish blob is clearly visible.
+ - Indicates strong consistency in pose, orientation, and background.
+
+
+ **Implications for Modeling**
+
+ - **Whole**:
+ - High consistency makes it easier for models to learn.
+ - Ideal for CNNs, as it's well-centered and uniformly structured objects.
+
+ - **Sliced & Halved**:
+ - Require additional preprocessing or augmentation.
+ - **Higher intra-class variation** may lower model performance if not addressed.
+ """)
+
+
+ st.markdown("""
+ ##### **Average Image Analysis: Pear (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_avg_pear.png", caption="Average image Pear variations", use_container_width=True)
+
+
+ st.markdown("""
+ **Visual Observations**
+
+ 1. **Halved**
+ - The soft yellow-green blob is relatively centered but very diffused.
+ - This suggests that while objects are roughly centered, their orientation, scale, and cropping vary significantly.
+
+ 2. **Sliced**
+ - The yellow region is more centralized and denser than in the halved class, indicating better consistency in object placement across samples.
+ - However, the blur indicates that slices still vary in size, number, and arrangement.
+
+ 3. **Whole**
+ - The bright yellow-green blob is the most prominent and sharply centered.
+ - Strong evidence of consistent centering, scale, and posture.
+ - Least blur, indicating high uniformity across samples. which may lead to overfitting and reduced generalization.
+
+
+ **Implications for Modeling**
+
+ - All three classes appear to be roughly centered, which means the model might struggle with challenging or unusual positioning.
+ """)
+
+ st.markdown("""
+ ##### **Average Image Analysis: Strawberry (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_avg_strawberry.png", caption="Average image Strawberry variations", use_container_width=True)
+
+
+ st.markdown("""
+ **Visual Observations**
+
+ 1. **Hulled**
+ - The average image has a compact red blob at the center.
+ - This indicates that most hulled strawberries are consistently centered and aligned.
+
+ 2. **Sliced**
+ - The average image appears more orange and diffuse compared to hulled.
+ - This suggests a higher variation in slice count, thickness, or arrangement.
+ - The blur shows the slices are still mostly centered but vary in shape and coverage.
+
+ 3. **Whole**
+ - The average image shows a slightly darker, rounder red blob than hulled.
+ - It is well-centered and more uniform than sliced.
+ - Indicates some variation in pose or camera angle, but overall still consistent around center.
+
+ **Implications for Modeling**
+
+ - All three categories have well-centered objects and also show some variation in position. This makes it easier for models to learn and extract features, especially due to the consistent central positioning, while also enabling learning under challenging conditions.
+ """)
+
+
+ st.markdown("""
+ ##### **Average Image Analysis: Tomato (Intra-Class Variations)**""")
+
+ st.image("assets/images/part2_avg_tomato.png", caption="Average image tomato variations", use_container_width=True)
+
+ st.markdown("""
+ **isual Observations**
+
+ 1. **Diced**
+ - Multiple reddish blobs are visible but still form a centralized mass.
+ - This indicates that diced tomatoes, while individually small, are often grouped toward the center across samples.
+ - Shows moderate variation in shape and number, but not in placement.
+
+ 2. **Vines**
+ - A distinct red cluster appears at the center, surrounded by subtle textures.
+ - This suggests tomatoes on vines are generally centered, but with extra visual components (stems, leaves) adding background complexity.
+ - Moderate blur indicates some variability in orientation and scene layout.
+
+ 3. **Whole**
+ - A very defined and uniform circular red blob is present at the center.
+ - Suggests consistent centre aligment and pose across images.
+
+ **Implications for Modeling**
+
+ - All three classes exhibit **strong central alignment**. For better generalization, it is preferable that the central blob appears more blurred, as this indicates diverse object positioning and conditions, which helps the model perform well across varied scenarios.
+ - Despite differences in object structure, the consistent central positioning across all three classes allows models to effectively learn spatially anchored features, but may cause the model to struggle when objects appear in different positions or when the image and it's background is complex.
+ """)
+
+
+ st.markdown("### 3.4 Image Analysis conclusion")
+
+ st.markdown("""
+ The combination of average images and RGB histogram plots reveals that, in general, all classes (onion, pear, strawberry, tomato) demonstrate a strong central focus in their average images. This is ideal for convolutional neural networks (CNNs), which exploit spatial locality. However, such consistency may limit generalization to real-world, off-centered samples.
+
+ ###### Insights by Visual Feature
+
+ | Feature | Observation | Implication |
+ |----------------------------|------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
+ | **Positioning** | Most images are well-centered across all classes and variations. | Models will learn easily but may fail under real-world misalignment. |
+ | **Blur in Average Images** | Indicates intra-class variation. More **blur = more diversity** in orientation, size, and structure. **Onion** and **strawberry** show the most blur among intra-class variations. | Helps generalization if balanced; too little variation risks overfitting. |
+ | **Histogram Shape** | Distinct peaks indicate color dominance and exposure levels. It's observed consistently across all classes. | Highlights the importance of using **RGB** channels as input. |
+ | **Background Consistency** | Pear and tomato classes tend to have cleaner backgrounds. | Models trained on these may struggle with background clutter in real-world scenarios. |
+
+ While the dataset provides clean and centered images conducive to initial model training, there is a **risk of overfitting to ideal conditions**.
+ """)
+
+
+
+
+
+ st.markdown("### 4.5 Training and Results")
+
+ st.markdown("""
+ We used a dataset of **3,000 manually labeled images per class**, with 1,000 images for each intra-class variation β **whole**, **halved/hulled**, and **sliced** β across four categories: **tomato**, **onion**, **pear**, and **strawberry**.
+
+ The dataset was split using either a **60:20:20 ratio** or, in some cases, a **50:25:25 ratio** for training, validation, and testing, respectively.
+ """)
+
+ col1, col2 = st.columns(2)
+
+ with col1:
+ st.markdown("##### Onion & Strawberry")
+ st.markdown("""
+ - **Training:** 1,500 images β **6,000 after augmentation**
+ - **Validation:** ~700 images
+ - **Testing:** ~700 images
+ - **Optimizer:** Adam
+ - **Learning Rate:** 0.0001
+ """)
+
+ with col2:
+ st.markdown("##### Pear & Tomato")
+ st.markdown("""
+ - **Training:** 1,827 images
+ - **Validation:** ~600 images
+ - **Testing:** ~600 images
+ - **Optimizer:** Adam
+ - **Learning Rate:** 0.0001
+ """)
+
+ st.markdown("""
+ For **strawberry** and **onion**, we applied data augmentation using image rotations (90Β°, 180Β°, and 270Β°), increasing the training samples from **1,500 to 6,000**, while keeping the **validation and test sets at approximately 700 images** each.
+
+ **Why augmentation for onion and strawberry?**
+ From the average image analysis, these classes showed **higher visual noise and blur**, indicating significant intra-class variation. Without augmentation, the model risked **overfitting to noise** and generalizing poorly. Rotation-based augmentation helped expose the model to diverse orientations and reduce this risk.
+
+ **Why no augmentation for pear and tomato?**
+ Our analysis of their average images and RGB histograms revealed that these classes were **well-centered**, **well-lit**, and had **limited background variation**. As a result, the model could learn from them effectively without augmentation. Although classification performance may degrade in real-world scenarios with cluttered or complex backgrounds, in **ideal settings** β where images are centered and consistently lit β these classes are expected to yield **strong performance even without augmentation**.
+
+ Due to hardware constraints (GPU memory limits), we used a **batch size of 32**. We also implemented **early stopping** with a patience of 3 epochs β meaning training stops if no improvement is seen in validation accuracy for 5 consecutive epochs.
+ """)
+
+
st.markdown("""
- This term project serves primarily as an educational exercise aimed at giving
- end-to-end exposure to building a modern NLP system. Our goal is to construct a semantic
- recipe-search engine that demonstrates how domain-specific fine-tuning of BERT can
- substantially improve retrieval quality over simple keyword matching.
+ ##### Model Performance Summary
+ """)
+ st.markdown("""
+ We saved the model with the **highest validation accuracy** and the **smallest difference between training and validation accuracy** to avoid any form of overfitting.
+ """)
+
+
+
+ # Insert training & validation graph
+ col1, col2 = st.columns([1,1]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part2_onion_graph.png", caption="**Onion**: Training vs Validation Loss and Accuracy", use_container_width=True)
+ with col2:
+ st.image("assets/images/part2_pear_graph.png", caption="**Pear**: Training vs Validation Loss and Accuracy", use_container_width=True)
- **Key Contributions:**
- - A cleaned, category-labelled recipe subset of 15,000 recipes
- - Training scripts that yield adapted contextual embeddings
- - A production-ready retrieval service that returns top-K most relevant recipes
- - Comparative evaluation against classical baselines
+ col1, col2 = st.columns([1,1]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part2_strawberry_graph.png", caption="**Strawberry**: Training vs Validation Loss and Accuracy", use_container_width=True)
+ with col2:
+ st.image("assets/images/part2_tomato_graph.png", caption="**Tomato**: Training vs Validation Loss and Accuracy", use_container_width=True)
+
+ st.markdown("""
+ The above graph shows the training and validation accuracy and loss curves. We can observe that **Pear** and **Tomato** reached 100% validation accuracy within **one** epochs, whereas **Onion** and **Strawberry** took longer to achieve high accuracy.
+ Despite the initial differences, all models eventually reached **very good performance**, and their detailed classification reports are provided below.
+ """)
+ col1, col2, col3 = st.columns([0.3,2,0.3])
+ with col2:
+ st.image("assets/images/part2_combined_report.png", caption="Model Report", use_container_width=True)
+
+ st.markdown("""
+ All models achieved very high accuracy:
+
+ - **Pear & Tomato:** 100% test accuracy
+ - **Onion & Strawberry:** ~98.6% test accuracy
+
+ These results show that the dataset was clean, well-labeled, and had consistent object placement.
+ However, **100%** test accuracy may indicate lack of real-world complexity in the test set.
+
+ **Overall:** The models perform extremely well under ideal conditions.
+ """)
+
+
+ st.markdown("""
+ #### False Positives / False Negatives
+ The confusion matrix provides insights into how our model performed, including whether it made any misclassifications and, if so, between which classes the confusion occurred.
+ """)
+
+ col1, col2, col3, col4 = st.columns([1,1,1,1]) # small column left, larger right
+ with col1:
+ st.image("assets/images/part2_cm_onion.png", caption="**Onion**: confusion matrix", use_container_width=True)
+ with col2:
+ st.image("assets/images/part2_cm_pear.png", caption="**Pear**: confusion matrix", use_container_width=True)
+ with col3:
+ st.image("assets/images/part2_cm_strawberry.png", caption="**Strawberry**: confusion matrix", use_container_width=True)
+ with col4:
+ st.image("assets/images/part2_cm_tomato.png", caption="**Tomato**: confusion matrix", use_container_width=True)
+
+ st.markdown("""
+ ###
+ 1. **Onion**
+ The model shows strong overall performance but made a few misclassifications between **halved β whole** and **sliced β halved**, suggesting slight confusion due to visual similarity in edge cases.
+
+ 2. **Strawberry**
+ Minor confusion is observed between **hulled and whole**, likely due to similar color and shape. Still, the model maintains excellent overall accuracy and balance.
+
+ 3. **Pear**
+ Perfect classification across all classes β no false positives or false negatives β reflecting highly consistent, separable visual features in the dataset.
+
+ 4. **Tomato**
+ No misclassifications were made; the model distinguishes **diced, sliced, and whole** tomatoes perfectly β likely due to strong shape and texture differences across classes.
+ """)
+
+ st.markdown("""
+ ##### Visual Analysis of FN/FP
+ We know that there are no misclassifications for **pear** and **tomato**, but there are some for **strawberry** and **onion**.
+ By examining the misclassified images, we can determine whether these are edge cases or visually complex examples.
+ This helps us understand whether the model has learned the important features or is also misclassifying simple, obvious images.
+ """)
+
+ col1, col2 = st.columns([0.92,1])
+
+ with col1:
+ st.image("assets/images/part2_fn_onion.png", caption="FP/FN for Onion", use_container_width=True)
+
+ with col2:
+ st.image("assets/images/part2_fn_strawberry.png", caption="FP/FN for Strawberry", use_container_width=True)
+
+ st.markdown("""
+ The FP/FN examples for **onion** and **strawberry** reveal that the model often struggles with **borderline or visually ambiguous cases**. These cases can be a bit ambiguous for humans as well.
+
+ - For **onions**, many misclassified examples involve **poor lighting**, **background clutter**, or **partial views** of the object (e.g., close-up or occluded views of halved onions).
+ - For **strawberries**, the model tends to confuse **hulled and sliced** variants. This likely happens due to **similar color/texture**, especially when slicing is mistaken for the top of a hulled image. Some misclassified examples also show **hands or objects in the frame**, indicating that **background noise affects classification**.
+
+ Overall, these misclassifications imply that the model performs well on clean, canonical examples but may falter under **variation in lighting or occlusion**.
+ """)
+
+
+
+ st.markdown("""
+ """, unsafe_allow_html=True)
+
+
+
+ st.markdown("""
+ #### Learned Feature Maps (Pattern) Analysis
+
+ To understand what our model has actually learned and how it perceives different food items internally, we visualized **feature maps** extracted from various convolutional layers of **EfficientNet-B0**.
+
+ The image below shows the **single most-activated channel per layer** for each intraclass variation β whole, halved/hulled, and sliced β of the main classes: Onion, Pear, Tomato, and Strawberry, across **9 convolutional stages**.
""")
+
+ st.markdown("""
+ ##### **Onion** Intra-class Map Analysis
+ """, unsafe_allow_html=True)
+
+ st.image("assets/images/part2_map_onion.png", caption="Onion: Channels each layer", use_container_width=True)
+
+ st.markdown("""
+ - **Whole:** Initial layers clearly capture the round bulb shape and strong edge details. As we move deeper, the model focuses on inner textures and center activation.
+ - **Halved:** Earlier layers detect circular contours well. Deeper layers show more dispersed activations.
+ - **Sliced:** Earlier layers isolate circular ring patterns effectively. Later layers show more defined and strong central activations.
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ ##### **Pear** Intra-class Map Analysis
+ """, unsafe_allow_html=True)
+
+ st.image("assets/images/part2_map_pear.png", caption="Pear: Channels each layer", use_container_width=True)
+ st.markdown("""
+ - **Whole:** Early layers highlight the pear shape and lighting edges. Strong attention is given to the vertical body. Later layers retain this spatial focus.
+ - **Halved:** Feature maps capture the internal seed cavity and split texture effectively. Consistent center-focused activation is observed.
+ - **Sliced:** Although flat in shape, sliced pears still maintain good feature flow. It also shows center-focused activation.
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ ##### **Strawberry** Intra-class Map Analysis
+ """, unsafe_allow_html=True)
+
+ st.image("assets/images/part2_map_Strawberry.png", caption="Strawberry: Channels each layer", use_container_width=True)
+
+ st.markdown("""
+ - **Whole:** Attention on object edges and shadows in early layers. Later activations begin to focus more on object.
+ - **Hulled:** Strong and crisp focus on object boundaries across all layers. Highlights strawberry contours and texture clearly.
+ - **Sliced:** Recognizes inner structure and scattered placement. Centralized patches persist in deeper layers, indicating successful encoding of sliced textures.
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ ##### **Tomato** Intra-class Map Analysis
+ """, unsafe_allow_html=True)
+
+
+ st.image("assets/images/part2_map_Tomato.png", caption="Tomato: Channels each layer", use_container_width=True)
+
+ st.markdown("""
+ - **Whole:** High activation on elliptical shape and color gradient. Mid and deep layers preserve tomato body well.
+ - **Vines:** Early layers capture fine vine structures and deeper layers focuses well on the object.
+ - **Diced:** Early stages show multiple sharp activations on cut surfaces. Later stages focus cleanly on central parts with well-formed feature blocks.
+ """, unsafe_allow_html=True)
+
+ st.markdown("""
+ **Key Takeaways:**
+
+ - The model **adaptively recognizes visual patterns** across variations within the same class.
+ - It **leverages shape consistency and repetitive textures** (like rings, seeds, or slices) for confident predictions.
+ - It handles **cluttered or occluded cases** reasonably well but shows minor degradation when the context becomes noisy (e.g., packaging, hands, lighting shadows).
+
+ This reinforces that while the model is robust to moderate noise, it **performs best on clean, centered, and clearly structured examples**. It also suggests that our dataset is mostly composed of clean, centered images with a few complex variations.
+ """)
+
+
# Dataset and Preprocessing
- st.subheader("Dataset and Pre-processing")
+ st.subheader("5. NLP Pipeline")
st.markdown("""
- **Data Sources:**
+ #### 5.1 Data Sources:
The project draws from two CSV files:
- **Raw_recipes.csv:** 231,637 rows, one per recipe with columns: *id, name, ingredients, tags, minutes, steps, description, n_steps, n_ingredients*
@@ -58,7 +941,7 @@ def render_report():
""")
st.markdown("""
- **Corpus Filtering and Subset Selection**
+ #### 5.2 Corpus Filtering and Subset Selection
- **Invalid rows removed:** recipes with empty ingredient lists, missing tags, or fewer than three total tags
- **Random sampling:** 15,000 recipes selected for NLP fine-tuning
@@ -67,7 +950,7 @@ def render_report():
""")
st.markdown("""
- **Text Pre-processing Pipeline**
+ #### 5.3 Text Pre-processing Pipeline
- **Lower-casing & punctuation removal:** normalized to lowercase, special characters stripped
- **Stop-descriptor removal:** culinary modifiers (*fresh, chopped, minced*) and measurements (tablespoons, teaspoons, cups, etc.) removed
@@ -76,7 +959,8 @@ def render_report():
- **Tokenization:** standard *bert-base-uncased* WordPiece tokenizer, sequences truncated/padded to 128 tokens
""")
# Technical Specifications
- st.subheader("Technical Specifications")
+ st.markdown("""
+ #### 5.4 Technical Specifications""")
col1, col2 = st.columns(2)
with col1:
st.markdown("""
@@ -94,11 +978,9 @@ def render_report():
- Transformers 4.38
- Google Colab A100 GPU
""")
- # Methodology
- st.subheader("Methodology")
st.markdown("""
- **Model Architecture**
+ #### 5.5 Model Architecture
- **Base Model:** bert-base-uncased
- **Additional Layers:** In some runs, we added a single linear classification layer with dropout (p = 0.1)
@@ -111,14 +993,9 @@ def render_report():
results were better than before, it still was not good in indentifying the relashionships between ingredients and the different tags. We then further
structured the data by ordering the tags and ingredients in a strcutured manner across the dataset and retrained the model. This resulted in a better
training and validation loss. This is also evident in the semantic retrieval results below.
-
- **Website Development:**
- - We used streamlit to develop the websit. However, we faced few issues with the size of the trained model and we switched hosting to Hugging Face.
- - The website loades the pre-trained model along with recipes embeddings and top-k retrieval function and waits for the user to enter a query.
- - The query is then processed b the model and top-k recipes are returned.
""")
- st.markdown("**Hyperparameters and Training**")
+ st.markdown("#### 5.6 Hyperparameters and Training")
col1, col2 = st.columns(2)
with col1:
st.markdown("""
@@ -136,7 +1013,7 @@ def render_report():
""")
# Mathematical Formulations
- st.subheader("Mathematical Formulations and Top-K Retrieval")
+ st.markdown("##### Mathematical Formulations and Top-K Retrieval")
st.markdown("""**Query Embedding and Similarity Calculation**: we used the trained model weights to generate embeddings for the entire recipe corpus. We then used cosine similarity to calculate the similarity between the query and the recipe corpus.
and once the user query is passed, we embedded the querry using the trained model and used the cosine similarity formula below to retrieve the top-K
@@ -146,10 +1023,11 @@ def render_report():
\text{Similarity}(q, r_i) = \cos(\hat{q}, \hat{r}_i) = \frac{\hat{q} \cdot \hat{r}_i}{\|\hat{q}\|\|\hat{r}_i\|}
""")
st.markdown("Where $\\hat{q}$ is the BERT embedding of the query, and $\\hat{r}_i$ is the embedding of the i-th recipe.")
-
-
+
+
# Results
- st.subheader("Results")
+ st.markdown("#### 5.7 Results")
+
st.markdown("**Training and Validation Loss**")
results_data = {
@@ -193,7 +1071,7 @@ def render_report():
""")
# Discussion and Conclusion
- st.subheader("Discussion and Conclusion")
+ st.markdown("#### Discussion and Conclusion")
st.markdown("""
The experimental evidence underscores the importance of disciplined pre-processing when
adapting large language models to niche domains. The breakthrough came with ingredient-ordering
@@ -215,7 +1093,7 @@ def render_report():
""")
# References
- st.subheader("References")
+ st.markdown("### 6. References")
st.markdown("""
[1] Vaswani et al., "Attention Is All You Need," NeurIPS, 2017.
[2] Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," NAACL-HLT, 2019.
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new file mode 100644
index 0000000000000000000000000000000000000000..991173f2f04e8e4605f1f74b7b0911da2ee0df16
--- /dev/null
+++ b/scripts/CV/Part1.ipynb
@@ -0,0 +1,946 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b70151d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import torch.nn as nn\n",
+ "from PIL import Image\n",
+ "import torch.nn.functional as F\n",
+ "import torchvision.models as models\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from torchvision import models\n",
+ "from torchvision.models import efficientnet_b0, EfficientNet_B0_Weights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "445679c7",
+ "metadata": {},
+ "source": [
+ "### Functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c37cc27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
+ " images = []\n",
+ " for root, _, files in os.walk(folder_path):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " try:\n",
+ " img_path = os.path.join(root, file)\n",
+ " img = Image.open(img_path).convert(\"RGB\")\n",
+ " img = img.resize(image_size)\n",
+ " images.append(np.array(img))\n",
+ " except Exception as e:\n",
+ " print(f\"Failed on {img_path}: {e}\")\n",
+ " return np.array(images)\n",
+ "\n",
+ "def plot_rgb_histogram_subplot(ax, images, class_name):\n",
+ " sample = images[random.randint(0, len(images) - 1)]\n",
+ " colors = ('r', 'g', 'b')\n",
+ " for i, col in enumerate(colors):\n",
+ " hist = np.histogram(sample[:, :, i], bins=256, range=(0, 256))[0]\n",
+ " ax.plot(hist, color=col)\n",
+ " ax.set_title(f\"RGB Histogram β {class_name.capitalize()}\")\n",
+ " ax.set_xlabel(\"Pixel Value\")\n",
+ " ax.set_ylabel(\"Frequency\")\n",
+ " \n",
+ "def augment_rotations(X, y):\n",
+ " X_aug = []\n",
+ " y_aug = []\n",
+ " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
+ " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
+ " X_aug.append(X_rot)\n",
+ " y_aug.append(y.clone()) # Same labels for rotated images\n",
+ " return torch.cat(X_aug), torch.cat(y_aug)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17c5b6fa",
+ "metadata": {},
+ "source": [
+ "### Dataset Location"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f833049",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onion_folder = \"dataset/Onion_512\"\n",
+ "strawberry_folder = \"dataset/Strawberry_512\"\n",
+ "pear_folder = \"dataset/Pear_512\"\n",
+ "tomato_folder = \"dataset/Tomato_512\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "afce8611",
+ "metadata": {},
+ "source": [
+ "### loading dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e913838",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onion_images = load_images_from_folder(onion_folder)\n",
+ "strawberry_images = load_images_from_folder(strawberry_folder)\n",
+ "pear_images = load_images_from_folder(pear_folder)\n",
+ "tomato_images = load_images_from_folder(tomato_folder)\n",
+ "\n",
+ "print(\"onion_images:\", onion_images.shape)\n",
+ "print(\"strawberry_images:\", strawberry_images.shape)\n",
+ "print(\"pear_images:\", pear_images.shape)\n",
+ "print(\"tomato_images:\", tomato_images.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07f5d12e",
+ "metadata": {},
+ "source": [
+ "Each of our classes have got around ~3000 samples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80e9ecc3",
+ "metadata": {},
+ "source": [
+ "### Visualizing image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00149f35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "datasets = {\n",
+ " \"onion\": onion_images,\n",
+ " \"strawberry\": strawberry_images,\n",
+ " \"pear\": pear_images,\n",
+ " \"tomato\": tomato_images\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def show_random_samples(images, class_name, count=5):\n",
+ " indices = random.sample(range(images.shape[0]), count)\n",
+ " selected = images[indices]\n",
+ "\n",
+ " plt.figure(figsize=(10, 2))\n",
+ " for i, img in enumerate(selected):\n",
+ " plt.subplot(1, count, i+1)\n",
+ " plt.imshow(img.astype(np.uint8))\n",
+ " plt.axis('off')\n",
+ " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "# Display for each class\n",
+ "for class_name, image_array in datasets.items():\n",
+ " show_random_samples(image_array, class_name)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab765929",
+ "metadata": {},
+ "source": [
+ "### Getting RGB pixel count per class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dcafbe0c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, len(datasets), figsize=(20, 5))\n",
+ "\n",
+ "for ax, (class_name, images) in zip(axes, datasets.items()):\n",
+ " plot_rgb_histogram_subplot(ax, images, class_name)\n",
+ " ax.label_outer() # Hide x labels and tick labels for inner plots\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a565dee",
+ "metadata": {},
+ "source": [
+ "## RGB Histogram Analysis: What It Tells Us About the Dataset\n",
+ "\n",
+ "This RGB histogram plot shows the **distribution of pixel intensities** for the **Red, Green, and Blue channels** in one sample image per class (`Onion`, `Strawberry`, `Pear`, `Tomato`). \n",
+ "Itβs a **visual summary of color composition** and can reveal important patterns about your dataset.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### π General Insights\n",
+ "\n",
+ "#### 1. Class Color Signatures\n",
+ "Each class has a unique RGB distribution:\n",
+ "\n",
+ "- The model can learn to **distinguish classes based on color patterns**.\n",
+ "- **Example:**\n",
+ " - `Tomato`: Strong red peaks.\n",
+ " - `Pear`: Dominant green and blue bands.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### 2. Image Quality / Noise\n",
+ "Unusual spikes or flat histograms may indicate:\n",
+ "\n",
+ "- **Overexposed or underexposed images**.\n",
+ "- **Noisy or poor-quality samples** (e.g., background dominates the image).\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### 3. π Channel Dominance / Balance\n",
+ "Histogram analysis helps decide:\n",
+ "\n",
+ "- Should we **convert to grayscale**? \n",
+ " (Useful if R, G, B histograms are nearly identical.)\n",
+ "- As we see in majority of classes the R,G,B variation is distinct(in onion it's almost the same), hence we need RGB channles in input\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### π Per-Class Histogram Analysis\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### π§ Onion\n",
+ "- **Red & Green:** Sharp peaks around 140β150.\n",
+ "- **Blue:** Dominant with a broad peak around 100.\n",
+ "- **Interpretation:**\n",
+ " - Likely represents white/yellow onion layers with subtle shadows.\n",
+ " - Dominant blue may come from lighting or background.\n",
+ "- **Implications:**\n",
+ " - The model may learn to detect **mid-range blue with sharp red-green peaks**.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### π Strawberry\n",
+ "- **Red:** Strong peaks at ~80 and ~220.\n",
+ "- **Green & Blue:** Broader and less frequent.\n",
+ "- **Interpretation:**\n",
+ " - High red intensity is consistent with strawberry skin.\n",
+ " - Low blue confirms lack of bluish tones.\n",
+ "- **Implications:**\n",
+ " - A **very color-distinct class**.\n",
+ " - The model can learn it easily with minimal augmentation.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### π Pear\n",
+ "- **Green & Blue:** Peaks between 50β120.\n",
+ "- **Red:** Moderate and broad around 100β150.\n",
+ "- **Interpretation:**\n",
+ " - Pear skin includes light green/yellow shades with reflections.\n",
+ " - Background or lighting likely increases blue response.\n",
+ " - All three channels show similar trends, suggesting: Minimal variation in pear color, and Uniform background and illumination conditions\n",
+ "- **Implications:**\n",
+ " - Not much background variation in pear\n",
+ "\n",
+ "---\n",
+ "\n",
+ "#### π Tomato\n",
+ "- **Red:** Extremely sharp peak at ~120.\n",
+ "- **Green & Blue:** Very low, drop sharply after 100.\n",
+ "- **Interpretation:**\n",
+ " - Strongly saturated red β characteristic of ripe tomatoes.\n",
+ "- **Implications:**\n",
+ " - **Highly distinguishable** via color alone.\n",
+ " - Risk of **overfitting to red features** if background is red.\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "146e8b61",
+ "metadata": {},
+ "source": [
+ "## Average image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42fca26e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = list(datasets.keys())\n",
+ "num_classes = len(class_names)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, num_classes, figsize=(4 * num_classes, 4)) # 1 row, 4 columns\n",
+ "\n",
+ "for i, (class_name, images) in enumerate(datasets.items()):\n",
+ " avg_img = np.mean(images.astype(np.float32), axis=0)\n",
+ " axes[i].imshow(avg_img.astype(np.uint8))\n",
+ " axes[i].set_title(f\"Average Image β {class_name.capitalize()}\")\n",
+ " axes[i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ba3e21f",
+ "metadata": {},
+ "source": [
+ "# Dataset Analysis Based on Average Images\n",
+ "\n",
+ "The average images of **Onion**, **Strawberry**, **Pear**, and **Tomato** offer valuable insights into the characteristics of the dataset they were generated from.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## General Observations\n",
+ "\n",
+ "1. **Blurriness of All Average Images** \n",
+ " - The high level of blur suggests that the objects (fruits/vegetables) vary significantly in position, orientation, and size within the images.\n",
+ " - There is no consistent alignment or cropping β objects appear in different parts of the frame across the dataset.\n",
+ "\n",
+ "2. **Centered Color Blobs** \n",
+ " - Each average image displays a dominant color region toward the center:\n",
+ " - π§ Onion: pale pinkish-grey center\n",
+ " - π Strawberry: red core\n",
+ " - π Pear: yellow-green diffuse center\n",
+ " - π Tomato: reddish-orange with surrounding brown-green\n",
+ " - This suggests that despite variation, most objects are somewhat centered in their respective images.\n",
+ " - For pear and tomato, the shape and color are more distinct and localized in the average image. This suggests that in most of these images, the required object was centered with less positional variation. In contrast, for onion and strawberry, the increased blurriness and less defined color blobs suggest more positional variation.\n",
+ "\n",
+ "3. **Background Color and Texture** \n",
+ " - All images share a similar gray-brown background tone.\n",
+ " - This implies the dataset likely includes a variety of natural or neutral-colored backgrounds (e.g., kitchen settings, markets) rather than standardized white/black backgrounds.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## Implications for Model Training\n",
+ "\n",
+ "- **Color is a Strong Signal**\n",
+ " - Dominant colors are preserved in each average image, suggesting that color-based features will play a major role in classification models. Therefore, it is important to retain all three color channels as input features.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dec6064b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "# Combine data\n",
+ "X = np.concatenate([onion_images, strawberry_images, pear_images, tomato_images], axis=0)\n",
+ "y = (\n",
+ " ['onion'] * len(onion_images) +\n",
+ " ['strawberry'] * len(strawberry_images) +\n",
+ " ['pear'] * len(pear_images) +\n",
+ " ['tomato'] * len(tomato_images)\n",
+ ")\n",
+ "\n",
+ "# Normalizing image\n",
+ "X = X.astype(np.float32) / 255.0\n",
+ "X = np.transpose(X, (0, 3, 1, 2)) # (N, C, H, W)\n",
+ "X_tensor = torch.tensor(X)\n",
+ "\n",
+ "le = LabelEncoder()\n",
+ "y_encoded = le.fit_transform(y)\n",
+ "y_tensor = torch.tensor(y_encoded)\n",
+ "\n",
+ "# splitting data into 50:25:25 (train, validation, test)\n",
+ "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.5, stratify=y_tensor, random_state=42)\n",
+ "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f265aea3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 32\n",
+ "\n",
+ "# Create new training dataset and loader\n",
+ "train_dataset = TensorDataset(X_train, y_train)\n",
+ "val_dataset = TensorDataset(X_val, y_val)\n",
+ "test_dataset = TensorDataset(X_test, y_test)\n",
+ "\n",
+ "# DataLoaders\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "36f26386",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del X_train, y_train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c469bc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
+ "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
+ "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50277bca",
+ "metadata": {},
+ "source": [
+ "## Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02440bb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_efficientnet_model(num_classes):\n",
+ " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
+ " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a516f06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"Using MPS (Apple GPU)\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print(\"MPS not available. Using CPU\")\n",
+ "\n",
+ "model = get_efficientnet_model(num_classes=4).to(device)\n",
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "criterion = nn.CrossEntropyLoss()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eb7d6007",
+ "metadata": {},
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "245a6709",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "best_val_acc = 0.0\n",
+ "train_losses = []\n",
+ "val_losses = []\n",
+ "train_accs = []\n",
+ "val_accs = []\n",
+ "epochs_no_improve = 0\n",
+ "early_stop = False\n",
+ "patience = 5\n",
+ "model_name = \"models/best_model_v1.pth\"\n",
+ "\n",
+ "for epoch in range(10):\n",
+ " if early_stop:\n",
+ " print(f\"Early stopping at epoch {epoch}\")\n",
+ " break\n",
+ " model.train()\n",
+ " total_train_loss = 0\n",
+ " train_correct = 0\n",
+ " train_total = 0\n",
+ "\n",
+ " for batch_x, batch_y in train_loader:\n",
+ " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
+ " preds = model(batch_x)\n",
+ " loss = criterion(preds, batch_y)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " total_train_loss += loss.item()\n",
+ "\n",
+ " # Track training accuracy\n",
+ " pred_labels = preds.argmax(dim=1)\n",
+ " train_correct += (pred_labels == batch_y).sum().item()\n",
+ " train_total += batch_y.size(0)\n",
+ "\n",
+ " train_accuracy = train_correct / train_total\n",
+ " avg_train_loss = total_train_loss / len(train_loader)\n",
+ " train_losses.append(avg_train_loss)\n",
+ " train_accs.append(train_accuracy)\n",
+ "\n",
+ " \n",
+ " model.eval()\n",
+ " val_correct = val_total = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_x, val_y in val_loader:\n",
+ " val_x, val_y = val_x.to(device), val_y.to(device)\n",
+ " val_preds = model(val_x).argmax(dim=1)\n",
+ " val_correct += (val_preds == val_y).sum().item()\n",
+ " val_total += val_y.size(0)\n",
+ "\n",
+ " val_accuracy = val_correct / val_total\n",
+ " validation_loss = criterion(model(val_x), val_y).item()\n",
+ "\n",
+ " # After calculating val_accuracy\n",
+ " val_losses.append(validation_loss)\n",
+ " val_accs.append(val_accuracy)\n",
+ "\n",
+ " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
+ " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
+ " if val_accuracy > best_val_acc:\n",
+ " best_val_acc = val_accuracy\n",
+ " torch.save(model.state_dict(), model_name)\n",
+ " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
+ " epochs_no_improve = 0\n",
+ " else:\n",
+ " epochs_no_improve += 1\n",
+ " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
+ "\n",
+ " if epochs_no_improve >= patience:\n",
+ " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
+ " early_stop = True\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abab0422",
+ "metadata": {},
+ "source": [
+ "### Loss and Accuracy Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bbab1d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epochs = range(1, len(train_losses) + 1)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
+ "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
+ "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930d22bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=4).to(device)\n",
+ "model.load_state_dict(torch.load(model_name))\n",
+ "model.eval() \n",
+ "\n",
+ "all_preds = []\n",
+ "all_targets = []\n",
+ "all_images = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch_x, batch_y in test_loader:\n",
+ " batch_x = batch_x.to(device)\n",
+ " preds = model(batch_x).argmax(dim=1).cpu()\n",
+ " all_preds.extend(preds.numpy())\n",
+ " all_targets.extend(batch_y.numpy())\n",
+ " all_images.extend(batch_x.cpu())\n",
+ "\n",
+ "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
+ "test_total = len(all_targets)\n",
+ "test_accuracy = test_correct / test_total\n",
+ "\n",
+ "print(f\"\\nTest Accuracy: {test_accuracy:.4f}\")\n",
+ "\n",
+ "target_names = le.classes_ # ['onion', 'pear', 'strawberry', 'tomato']\n",
+ "print(\"\\nClassification Report:\\n\")\n",
+ "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
+ "\n",
+ "cm = confusion_matrix(all_targets, all_preds)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
+ "plt.xlabel(\"Predicted Label\")\n",
+ "plt.ylabel(\"True Label\")\n",
+ "plt.title(\"Confusion Matrix\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0430479f",
+ "metadata": {},
+ "source": [
+ "## Sample FP, FN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4823498a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_preds = np.array(all_preds)\n",
+ "all_targets = np.array(all_targets)\n",
+ "all_images = torch.stack(all_images)\n",
+ "\n",
+ "for class_idx, class_name in enumerate(target_names):\n",
+ " print(f\"\\nShowing False Negatives and False Positives for class: {class_name}\")\n",
+ " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
+ " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
+ "\n",
+ " def show_images(indices, title, max_images=5):\n",
+ " num = min(len(indices), max_images)\n",
+ " if num == 0:\n",
+ " print(f\"No {title} samples.\")\n",
+ " return\n",
+ "\n",
+ " plt.figure(figsize=(12, 2))\n",
+ " for i, idx in enumerate(indices[:num]):\n",
+ " img = all_images[idx]\n",
+ " img = img.permute(1, 2, 0).numpy()\n",
+ " plt.subplot(1, num, i + 1)\n",
+ " plt.imshow((img - img.min()) / (img.max() - img.min()))\n",
+ " plt.axis('off')\n",
+ " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
+ " plt.suptitle(f\"{title} for {class_name}\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " show_images(fn_indices, \"False Negatives\")\n",
+ " show_images(fp_indices, \"False Positives\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "551cec6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0))\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # [C, H, W]\n",
+ " num_channels = min(fmap.shape[0], max_channels)\n",
+ "\n",
+ " plt.figure(figsize=(num_channels * 2, 2.5))\n",
+ " for i in range(num_channels):\n",
+ " plt.subplot(1, num_channels, i + 1)\n",
+ " plt.imshow(fmap[i], cmap='viridis')\n",
+ " plt.title(f\"{layer_name} ch{i}\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "147d63d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " # Register hooks for all layers in model.features\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0)) # Add batch dimension: [1, 3, 224, 224]\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # Shape: [C, H, W]\n",
+ "\n",
+ " # Compute mean activation per channel\n",
+ " channel_scores = fmap.mean(dim=(1, 2)) # [C]\n",
+ "\n",
+ " # Get indices of top-k channels\n",
+ " topk = torch.topk(channel_scores, k=min(max_channels, fmap.shape[0]))\n",
+ " top_indices = topk.indices\n",
+ "\n",
+ " # Plot top-k channels\n",
+ " plt.figure(figsize=(max_channels * 2, 2.5))\n",
+ " for idx, ch in enumerate(top_indices):\n",
+ " plt.subplot(1, max_channels, idx + 1)\n",
+ " plt.imshow(fmap[ch], cmap='viridis')\n",
+ " plt.title(f\"{layer_name}\\nch{ch.item()} ({channel_scores[ch]:.2f})\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6cc824a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=4)\n",
+ "model.load_state_dict(torch.load(\"models/best_model_v1.pth\"))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36044d25",
+ "metadata": {},
+ "source": [
+ "### Onion: Visulaize color channel "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07206168",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Onion_512/Whole/image_0001.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd24811d",
+ "metadata": {},
+ "source": [
+ "### Pear: Visulaize color channel "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "deb3981a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Pear_512/Whole/image_0089.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be9d8f98",
+ "metadata": {},
+ "source": [
+ "### Tomato: Visulaize color channel "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930ebe01",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Tomato_512/Whole/image_0001.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4769764",
+ "metadata": {},
+ "source": [
+ "### Strawberry: Visulaize color channel "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a45dc523",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Strawberry_512/Whole/image_0388.jpg\").convert(\"RGB\")\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img)\n",
+ "\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c695b7b6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/CV/compression.ipynb b/scripts/CV/compression.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a606aea888da8fa6a4187230e4f363efe57edfda
--- /dev/null
+++ b/scripts/CV/compression.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "77d13a9e",
+ "metadata": {},
+ "source": [
+ "224x224 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3018959e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from PIL import Image, ImageOps\n",
+ "\n",
+ "input_root = 'Tomato' # Root folder with raw images\n",
+ "output_root = 'Tomato_512' # Output root folder\n",
+ "os.makedirs(output_root, exist_ok=True)\n",
+ "\n",
+ "def process_image(input_path, output_path, size=(512, 512)):\n",
+ " try:\n",
+ " with Image.open(input_path) as img:\n",
+ " img = img.convert(\"RGB\")\n",
+ "\n",
+ " # Resize while preserving aspect ratio, then pad to 512x512\n",
+ " img = ImageOps.fit(img, size, Image.LANCZOS, centering=(0.5, 0.5))\n",
+ " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
+ " img.save(output_path, \"JPEG\", quality=95)\n",
+ " except Exception as e:\n",
+ " print(f\"β Error processing {input_path}: {e}\")\n",
+ "\n",
+ "# Recursively walk through input_root\n",
+ "for root, _, files in os.walk(input_root):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " input_path = os.path.join(root, file)\n",
+ " rel_path = os.path.relpath(input_path, input_root)\n",
+ " output_path = os.path.join(output_root, rel_path)\n",
+ " process_image(input_path, output_path)\n",
+ "\n",
+ "print(\"β All images processed and saved in\", output_root)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27f4b7b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from PIL import Image, ImageOps\n",
+ "\n",
+ "input_root = 'Onion' # Root folder with raw images\n",
+ "output_root = 'Onion_512' # Output root folder\n",
+ "os.makedirs(output_root, exist_ok=True)\n",
+ "\n",
+ "def process_image(input_path, output_path, size=(512, 512)):\n",
+ " try:\n",
+ " with Image.open(input_path) as img:\n",
+ " img = img.convert(\"RGB\")\n",
+ "\n",
+ " # Resize while preserving aspect ratio, then pad to 512x512\n",
+ " img = ImageOps.fit(img, size, Image.LANCZOS, centering=(0.5, 0.5))\n",
+ " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
+ " img.save(output_path, \"JPEG\", quality=95)\n",
+ " except Exception as e:\n",
+ " print(f\"β Error processing {input_path}: {e}\")\n",
+ "\n",
+ "# Recursively walk through input_root\n",
+ "for root, _, files in os.walk(input_root):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " input_path = os.path.join(root, file)\n",
+ " rel_path = os.path.relpath(input_path, input_root)\n",
+ " output_path = os.path.join(output_root, rel_path)\n",
+ " process_image(input_path, output_path)\n",
+ "\n",
+ "print(\"β All images processed and saved in\", output_root)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a0d918b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from PIL import Image, ImageOps\n",
+ "\n",
+ "input_root = 'Pear' # Root folder with raw images\n",
+ "output_root = 'Pear_512' # Output root folder\n",
+ "os.makedirs(output_root, exist_ok=True)\n",
+ "\n",
+ "def process_image(input_path, output_path, size=(512, 512)):\n",
+ " try:\n",
+ " with Image.open(input_path) as img:\n",
+ " img = img.convert(\"RGB\")\n",
+ "\n",
+ " # Resize while preserving aspect ratio, then pad to 512x512\n",
+ " img = ImageOps.fit(img, size, Image.LANCZOS, centering=(0.5, 0.5))\n",
+ " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
+ " img.save(output_path, \"JPEG\", quality=95)\n",
+ " except Exception as e:\n",
+ " print(f\"β Error processing {input_path}: {e}\")\n",
+ "\n",
+ "# Recursively walk through input_root\n",
+ "for root, _, files in os.walk(input_root):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " input_path = os.path.join(root, file)\n",
+ " rel_path = os.path.relpath(input_path, input_root)\n",
+ " output_path = os.path.join(output_root, rel_path)\n",
+ " process_image(input_path, output_path)\n",
+ "\n",
+ "print(\"β All images processed and saved in\", output_root)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1d5e72a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from PIL import Image, ImageOps\n",
+ "\n",
+ "input_root = 'Strawberry' # Root folder with raw images\n",
+ "output_root = 'Strawberry_512' # Output root folder\n",
+ "os.makedirs(output_root, exist_ok=True)\n",
+ "\n",
+ "def process_image(input_path, output_path, size=(512, 512)):\n",
+ " try:\n",
+ " with Image.open(input_path) as img:\n",
+ " img = img.convert(\"RGB\")\n",
+ "\n",
+ " # Resize while preserving aspect ratio, then pad to 512x512\n",
+ " img = ImageOps.fit(img, size, Image.LANCZOS, centering=(0.5, 0.5))\n",
+ " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
+ " img.save(output_path, \"JPEG\", quality=95)\n",
+ " except Exception as e:\n",
+ " print(f\"β Error processing {input_path}: {e}\")\n",
+ "\n",
+ "# Recursively walk through input_root\n",
+ "for root, _, files in os.walk(input_root):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " input_path = os.path.join(root, file)\n",
+ " rel_path = os.path.relpath(input_path, input_root)\n",
+ " output_path = os.path.join(output_root, rel_path)\n",
+ " process_image(input_path, output_path)\n",
+ "\n",
+ "print(\"β All images processed and saved in\", output_root)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd49ae48",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/CV/script.ipynb b/scripts/CV/script.ipynb
deleted file mode 100644
index 60dd3c7cc0c480790d64a108eb12041b77e451f6..0000000000000000000000000000000000000000
--- a/scripts/CV/script.ipynb
+++ /dev/null
@@ -1,738 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "1b70151d",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import numpy as np\n",
- "from PIL import Image\n",
- "import torch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "c37cc27b",
- "metadata": {},
- "outputs": [],
- "source": [
- "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
- " images = []\n",
- " for root, _, files in os.walk(folder_path):\n",
- " for file in files:\n",
- " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
- " try:\n",
- " img_path = os.path.join(root, file)\n",
- " img = Image.open(img_path).convert(\"RGB\")\n",
- " img = img.resize(image_size)\n",
- " images.append(np.array(img))\n",
- " except Exception as e:\n",
- " print(f\"Failed on {img_path}: {e}\")\n",
- " return np.array(images)\n",
- "\n",
- "def augment_rotations(X, y):\n",
- " X_aug = []\n",
- " y_aug = []\n",
- " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
- " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
- " X_aug.append(X_rot)\n",
- " y_aug.append(y.clone()) # Same labels for rotated images\n",
- " return torch.cat(X_aug), torch.cat(y_aug)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "3f833049",
- "metadata": {},
- "outputs": [],
- "source": [
- "onion_folder = \"Onion_512\"\n",
- "strawberry_folder = \"Strawberry_512\"\n",
- "pear_folder = \"Pear_512\"\n",
- "tomato_folder = \"Tomato_512\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "1e913838",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "onion_images: (2332, 224, 224, 3)\n",
- "strawberry_images: (2455, 224, 224, 3)\n",
- "pear_images: (3070, 224, 224, 3)\n",
- "tomato_images: (3046, 224, 224, 3)\n"
- ]
- }
- ],
- "source": [
- "\n",
- "\n",
- "onion_images = load_images_from_folder(onion_folder)\n",
- "strawberry_images = load_images_from_folder(strawberry_folder)\n",
- "pear_images = load_images_from_folder(pear_folder)\n",
- "tomato_images = load_images_from_folder(tomato_folder)\n",
- "\n",
- "print(\"onion_images:\", onion_images.shape)\n",
- "print(\"strawberry_images:\", strawberry_images.shape)\n",
- "print(\"pear_images:\", pear_images.shape)\n",
- "print(\"tomato_images:\", tomato_images.shape)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "00149f35",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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",
- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import random\n",
- "datasets = {\n",
- " \"onion\": onion_images,\n",
- " \"strawberry\": strawberry_images,\n",
- " \"pear\": pear_images,\n",
- " \"tomato\": tomato_images\n",
- "}\n",
- "\n",
- "\n",
- "def show_random_samples(images, class_name, count=5):\n",
- " indices = random.sample(range(images.shape[0]), count)\n",
- " selected = images[indices]\n",
- "\n",
- " plt.figure(figsize=(10, 2))\n",
- " for i, img in enumerate(selected):\n",
- " plt.subplot(1, count, i+1)\n",
- " plt.imshow(img.astype(np.uint8))\n",
- " plt.axis('off')\n",
- " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
- " plt.show()\n",
- "\n",
- "# Display for each class\n",
- "for class_name, image_array in datasets.items():\n",
- " show_random_samples(image_array, class_name)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "dec6064b",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import numpy as np\n",
- "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from sklearn.model_selection import train_test_split\n",
- "from torchvision import transforms\n",
- "\n",
- "# Combine data\n",
- "X = np.concatenate([onion_images, strawberry_images, pear_images, tomato_images], axis=0)\n",
- "y = (\n",
- " ['onion'] * len(onion_images) +\n",
- " ['strawberry'] * len(strawberry_images) +\n",
- " ['pear'] * len(pear_images) +\n",
- " ['tomato'] * len(tomato_images)\n",
- ")\n",
- "\n",
- "# Normalize and convert to torch tensors\n",
- "X = X.astype(np.float32) / 255.0\n",
- "X = np.transpose(X, (0, 3, 1, 2)) # (N, C, H, W)\n",
- "X_tensor = torch.tensor(X)\n",
- "\n",
- "# Encode labels\n",
- "le = LabelEncoder()\n",
- "y_encoded = le.fit_transform(y)\n",
- "y_tensor = torch.tensor(y_encoded)\n",
- "\n",
- "# Train/val/test split\n",
- "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.5, stratify=y_tensor, random_state=42)\n",
- "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f265aea3",
- "metadata": {},
- "outputs": [],
- "source": [
- "batch_size = 32\n",
- "\n",
- "X_augmented, y_augmented = augment_rotations(X_train, y_train)\n",
- "\n",
- "# Combine original and augmented data\n",
- "X_train_combined = torch.cat([X_train, X_augmented])\n",
- "y_train_combined = torch.cat([y_train, y_augmented])\n",
- "\n",
- "# Create new training dataset and loader\n",
- "\n",
- "train_dataset = TensorDataset(X_train, y_train)\n",
- "val_dataset = TensorDataset(X_val, y_val)\n",
- "test_dataset = TensorDataset(X_test, y_test)\n",
- "\n",
- "# DataLoaders\n",
- "\n",
- "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
- "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
- "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "36f26386",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "c469bc8d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "π’ Train Dataset: 21804 samples, 682 batches\n",
- "π’ Val Dataset: 2726 samples, 86 batches\n",
- "π’ Test Dataset: 2726 samples, 86 batches\n"
- ]
- }
- ],
- "source": [
- "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
- "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
- "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "02440bb8",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch.nn as nn\n",
- "import torch.nn.functional as F\n",
- "\n",
- "import torch.nn as nn\n",
- "import torchvision.models as models\n",
- "\n",
- "def get_efficientnet_model(num_classes):\n",
- " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
- "\n",
- " # Replace classifier head with custom head\n",
- " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
- "\n",
- " return model\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "6a516f06",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "β Using MPS (Apple GPU)\n"
- ]
- }
- ],
- "source": [
- "if torch.backends.mps.is_available():\n",
- " device = torch.device(\"mps\")\n",
- " print(\"β Using MPS (Apple GPU)\")\n",
- "else:\n",
- " device = torch.device(\"cpu\")\n",
- " print(\"β οΈ MPS not available. Using CPU\")\n",
- "\n",
- "model = get_efficientnet_model(num_classes=4).to(device)\n",
- "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
- "criterion = nn.CrossEntropyLoss()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "245a6709",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 01 | Train Loss: 0.0675 | Train Acc: 0.9790 | Val Acc: 0.9967\n",
- "New best model saved at epoch 1 with val acc 0.9967\n",
- "Epoch 02 | Train Loss: 0.0203 | Train Acc: 0.9939 | Val Acc: 0.9963\n",
- "No improvement for 1 epoch(s)\n",
- "Epoch 03 | Train Loss: 0.0226 | Train Acc: 0.9937 | Val Acc: 0.9956\n",
- "No improvement for 2 epoch(s)\n",
- "Epoch 04 | Train Loss: 0.0165 | Train Acc: 0.9956 | Val Acc: 0.9974\n",
- "New best model saved at epoch 4 with val acc 0.9974\n",
- "Epoch 05 | Train Loss: 0.0250 | Train Acc: 0.9941 | Val Acc: 0.9971\n",
- "No improvement for 1 epoch(s)\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[11], line 26\u001b[0m\n\u001b[1;32m 24\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 25\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m---> 26\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m total_train_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# Track training accuracy\u001b[39;00m\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/optimizer.py:485\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 481\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 482\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 483\u001b[0m )\n\u001b[0;32m--> 485\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 488\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/optimizer.py:79\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 77\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 79\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 81\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/adam.py:246\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 234\u001b[0m beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 236\u001b[0m has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[1;32m 237\u001b[0m group,\n\u001b[1;32m 238\u001b[0m params_with_grad,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 243\u001b[0m state_steps,\n\u001b[1;32m 244\u001b[0m )\n\u001b[0;32m--> 246\u001b[0m \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[43m \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 264\u001b[0m \u001b[43m \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 265\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoupled_weight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdecoupled_weight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/optimizer.py:147\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported..wrapper..maybe_fallback\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/adam.py:933\u001b[0m, in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, decoupled_weight_decay, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 931\u001b[0m func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[0;32m--> 933\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 936\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 940\u001b[0m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 941\u001b[0m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 942\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 944\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 945\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 946\u001b[0m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 947\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 948\u001b[0m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 949\u001b[0m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 950\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 951\u001b[0m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 952\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoupled_weight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecoupled_weight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 953\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Desktop/CNN training/myenv/lib/python3.9/site-packages/torch/optim/adam.py:439\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable, decoupled_weight_decay)\u001b[0m\n\u001b[1;32m 436\u001b[0m device_beta1 \u001b[38;5;241m=\u001b[39m beta1\n\u001b[1;32m 438\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m--> 439\u001b[0m \u001b[43mexp_avg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlerp_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdevice_beta1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;66;03m# Nested if is necessary to bypass jitscript rules\u001b[39;00m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m differentiable \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(beta2, Tensor):\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
- ]
- },
- {
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
- "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
- "\u001b[1;31mClick here for more info. \n",
- "\u001b[1;31mView Jupyter log for further details."
- ]
- }
- ],
- "source": [
- "best_val_acc = 0.0\n",
- "train_losses = []\n",
- "val_losses = []\n",
- "train_accs = []\n",
- "val_accs = []\n",
- "epochs_no_improve = 0\n",
- "early_stop = False\n",
- "patience = 3\n",
- "\n",
- "for epoch in range(10):\n",
- " if early_stop:\n",
- " print(f\"Early stopping at epoch {epoch}\")\n",
- " break\n",
- " model.train()\n",
- " total_train_loss = 0\n",
- " train_correct = 0\n",
- " train_total = 0\n",
- "\n",
- " for batch_x, batch_y in train_loader:\n",
- " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
- " preds = model(batch_x)\n",
- " loss = criterion(preds, batch_y)\n",
- "\n",
- " optimizer.zero_grad()\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- "\n",
- " total_train_loss += loss.item()\n",
- "\n",
- " # Track training accuracy\n",
- " pred_labels = preds.argmax(dim=1)\n",
- " train_correct += (pred_labels == batch_y).sum().item()\n",
- " train_total += batch_y.size(0)\n",
- "\n",
- " train_accuracy = train_correct / train_total\n",
- " avg_train_loss = total_train_loss / len(train_loader)\n",
- " train_losses.append(avg_train_loss)\n",
- " train_accs.append(train_accuracy)\n",
- "\n",
- " \n",
- " model.eval()\n",
- " val_correct = val_total = 0\n",
- "\n",
- " with torch.no_grad():\n",
- " for val_x, val_y in val_loader:\n",
- " val_x, val_y = val_x.to(device), val_y.to(device)\n",
- " val_preds = model(val_x).argmax(dim=1)\n",
- " val_correct += (val_preds == val_y).sum().item()\n",
- " val_total += val_y.size(0)\n",
- "\n",
- " val_accuracy = val_correct / val_total\n",
- " validation_loss = criterion(model(val_x), val_y).item()\n",
- "\n",
- " # After calculating val_accuracy\n",
- " val_losses.append(validation_loss)\n",
- " val_accs.append(val_accuracy)\n",
- "\n",
- " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
- " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
- " if val_accuracy > best_val_acc:\n",
- " best_val_acc = val_accuracy\n",
- " torch.save(model.state_dict(), \"best_model_v1.pth\")\n",
- " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
- " epochs_no_improve = 0\n",
- " else:\n",
- " epochs_no_improve += 1\n",
- " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
- "\n",
- " if epochs_no_improve >= patience:\n",
- " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
- " early_stop = True\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "3bbab1d8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "epochs = range(1, len(train_losses) + 1)\n",
- "\n",
- "plt.figure(figsize=(12, 5))\n",
- "\n",
- "# Plot Loss\n",
- "plt.subplot(1, 2, 1)\n",
- "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
- "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
- "plt.xlabel('Epoch')\n",
- "plt.ylabel('Loss')\n",
- "plt.title('Loss per Epoch')\n",
- "plt.legend()\n",
- "plt.grid(True)\n",
- "\n",
- "# Plot Accuracy\n",
- "plt.subplot(1, 2, 2)\n",
- "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
- "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
- "plt.xlabel('Epoch')\n",
- "plt.ylabel('Accuracy')\n",
- "plt.title('Accuracy per Epoch')\n",
- "plt.legend()\n",
- "plt.grid(True)\n",
- "\n",
- "plt.tight_layout()\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "930d22bd",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "β Test Accuracy: 0.9881\n",
- "\n",
- "π Classification Report:\n",
- "\n",
- " precision recall f1-score support\n",
- "\n",
- " onion 0.96 1.00 0.98 250\n",
- " pear 1.00 1.00 1.00 255\n",
- " strawberry 1.00 0.96 0.98 250\n",
- " tomato 1.00 1.00 1.00 255\n",
- "\n",
- " accuracy 0.99 1010\n",
- " macro avg 0.99 0.99 0.99 1010\n",
- "weighted avg 0.99 0.99 0.99 1010\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/var/folders/vj/mbznbr_s7fb1cqsqnx00ch_m0000gn/T/ipykernel_79754/1302256466.py:41: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n",
- " plt.tight_layout()\n",
- "/Users/saksham_lakhera/Desktop/CNN training/myenv/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n",
- " fig.canvas.print_figure(bytes_io, **kw)\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.metrics import classification_report, confusion_matrix\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "# Ensure model is in eval mode\n",
- "model.eval()\n",
- "\n",
- "# Collect all true and predicted labels\n",
- "all_preds = []\n",
- "all_targets = []\n",
- "all_images = []\n",
- "\n",
- "with torch.no_grad():\n",
- " for batch_x, batch_y in test_loader:\n",
- " batch_x = batch_x.to(device)\n",
- " preds = model(batch_x).argmax(dim=1).cpu()\n",
- " all_preds.extend(preds.numpy())\n",
- " all_targets.extend(batch_y.numpy())\n",
- " all_images.extend(batch_x.cpu())\n",
- "\n",
- "# Compute accuracy\n",
- "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
- "test_total = len(all_targets)\n",
- "test_accuracy = test_correct / test_total\n",
- "\n",
- "print(f\"\\nβ Test Accuracy: {test_accuracy:.4f}\")\n",
- "\n",
- "# Classification report\n",
- "target_names = le.classes_ # ['onion', 'pear', 'strawberry', 'tomato']\n",
- "print(\"\\nπ Classification Report:\\n\")\n",
- "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
- "\n",
- "# Confusion matrix\n",
- "cm = confusion_matrix(all_targets, all_preds)\n",
- "\n",
- "# Plot confusion matrix\n",
- "plt.figure(figsize=(6, 5))\n",
- "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
- "plt.xlabel(\"Predicted Label\")\n",
- "plt.ylabel(\"True Label\")\n",
- "plt.title(\"π Confusion Matrix\")\n",
- "plt.tight_layout()\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "4823498a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "π Showing False Negatives and False Positives for class: onion\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
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- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "π Showing False Negatives and False Positives for class: pear\n",
- "β No False Negatives samples.\n",
- "β No False Positives samples.\n",
- "\n",
- "π Showing False Negatives and False Positives for class: strawberry\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
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- "output_type": "display_data"
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "π Showing False Negatives and False Positives for class: tomato\n",
- "β No False Negatives samples.\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import torch\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "all_preds = np.array(all_preds)\n",
- "all_targets = np.array(all_targets)\n",
- "all_images = torch.stack(all_images) # shape: [N, C, H, W]\n",
- "\n",
- "# Per class FP and FN\n",
- "for class_idx, class_name in enumerate(target_names):\n",
- " print(f\"\\nπ Showing False Negatives and False Positives for class: {class_name}\")\n",
- "\n",
- " # False Negatives: True label is class_idx, but predicted something else\n",
- " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
- " # False Positives: Predicted class_idx, but true label is different\n",
- " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
- "\n",
- " def show_images(indices, title, max_images=5):\n",
- " num = min(len(indices), max_images)\n",
- " if num == 0:\n",
- " print(f\"β No {title} samples.\")\n",
- " return\n",
- "\n",
- " plt.figure(figsize=(12, 2))\n",
- " for i, idx in enumerate(indices[:num]):\n",
- " img = all_images[idx]\n",
- " img = img.permute(1, 2, 0).numpy() # [C, H, W] β [H, W, C]\n",
- " plt.subplot(1, num, i + 1)\n",
- " plt.imshow((img - img.min()) / (img.max() - img.min())) # normalize to [0,1] for display\n",
- " plt.axis('off')\n",
- " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
- " plt.suptitle(f\"{title} for {class_name}\")\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- "\n",
- " show_images(fn_indices, \"False Negatives\")\n",
- " show_images(fp_indices, \"False Positives\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "551cec6b",
- "metadata": {},
- "outputs": [],
- "source": [
- "## tbd"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "myenv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.21"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/scripts/CV/script_onion.ipynb b/scripts/CV/script_onion.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5af6ea94645c76a0d67a127749e8deea92408087
--- /dev/null
+++ b/scripts/CV/script_onion.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b70151d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "import seaborn as sns\n",
+ "import torchvision.models as models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c37cc27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
+ " images = []\n",
+ " for root, _, files in os.walk(folder_path):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " try:\n",
+ " img_path = os.path.join(root, file)\n",
+ " img = Image.open(img_path).convert(\"RGB\")\n",
+ " img = img.resize(image_size)\n",
+ " images.append(np.array(img))\n",
+ " except Exception as e:\n",
+ " print(f\"Failed on {img_path}: {e}\")\n",
+ " return np.array(images)\n",
+ "\n",
+ "def plot_rgb_histogram_subplot(ax, images, class_name):\n",
+ " sample = images[random.randint(0, len(images) - 1)]\n",
+ " colors = ('r', 'g', 'b')\n",
+ " for i, col in enumerate(colors):\n",
+ " hist = np.histogram(sample[:, :, i], bins=256, range=(0, 256))[0]\n",
+ " ax.plot(hist, color=col)\n",
+ " ax.set_title(f\"RGB Histogram β {class_name.capitalize()}\")\n",
+ " ax.set_xlabel(\"Pixel Value\")\n",
+ " ax.set_ylabel(\"Frequency\")\n",
+ "\n",
+ "def augment_rotations(X, y):\n",
+ " X_aug = []\n",
+ " y_aug = []\n",
+ " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
+ " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
+ " X_aug.append(X_rot)\n",
+ " y_aug.append(y.clone()) # Same labels for rotated images\n",
+ " return torch.cat(X_aug), torch.cat(y_aug)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f833049",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onion_halved = \"dataset/Onion_512/Halved\"\n",
+ "onion_sliced = \"dataset/Onion_512/Sliced\"\n",
+ "oion_whole = \"dataset/Onion_512/Whole\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e913838",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "onion_halved_images = load_images_from_folder(onion_halved)\n",
+ "onion_sliced_images = load_images_from_folder(onion_sliced)\n",
+ "oion_whole_images = load_images_from_folder(oion_whole)\n",
+ "\n",
+ "print(\"onion halved mages:\", onion_halved_images.shape)\n",
+ "print(\"onion sliced images:\", onion_sliced_images.shape)\n",
+ "print(\"oion whole images:\", oion_whole_images.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00149f35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datasets = {\n",
+ " \"halved\": onion_halved_images,\n",
+ " \"sliced\": onion_sliced_images,\n",
+ " \"whole\": oion_whole_images\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def show_random_samples(images, class_name, count=5):\n",
+ " indices = random.sample(range(images.shape[0]), count)\n",
+ " selected = images[indices]\n",
+ "\n",
+ " plt.figure(figsize=(10, 2))\n",
+ " for i, img in enumerate(selected):\n",
+ " plt.subplot(1, count, i+1)\n",
+ " plt.imshow(img.astype(np.uint8))\n",
+ " plt.axis('off')\n",
+ " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "# Display for each class\n",
+ "for class_name, image_array in datasets.items():\n",
+ " show_random_samples(image_array, class_name)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "36b0c3a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, len(datasets), figsize=(20, 5))\n",
+ "\n",
+ "for ax, (class_name, images) in zip(axes, datasets.items()):\n",
+ " plot_rgb_histogram_subplot(ax, images, class_name)\n",
+ " ax.label_outer() # Hide x labels and tick labels for inner plots\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c157569",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = list(datasets.keys())\n",
+ "num_classes = len(class_names)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, num_classes, figsize=(4 * num_classes, 4)) # 1 row, 4 columns\n",
+ "\n",
+ "for i, (class_name, images) in enumerate(datasets.items()):\n",
+ " avg_img = np.mean(images.astype(np.float32), axis=0)\n",
+ " axes[i].imshow(avg_img.astype(np.uint8))\n",
+ " axes[i].set_title(f\"Average Image β {class_name.capitalize()}\")\n",
+ " axes[i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dec6064b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datasets = {\n",
+ " \"halved\": onion_halved_images,\n",
+ " \"sliced\": onion_sliced_images,\n",
+ " \"whole\": oion_whole_images\n",
+ "}\n",
+ "\n",
+ "X = np.concatenate([onion_halved_images, onion_sliced_images, oion_whole_images], axis=0)\n",
+ "y = (\n",
+ " ['halved'] * len(onion_halved_images) +\n",
+ " ['sliced'] * len(onion_sliced_images) +\n",
+ " ['whole'] * len(oion_whole_images)\n",
+ ")\n",
+ "\n",
+ "X = X.astype(np.float32) / 255.0\n",
+ "X = np.transpose(X, (0, 3, 1, 2))\n",
+ "X_tensor = torch.tensor(X)\n",
+ "\n",
+ "le = LabelEncoder()\n",
+ "y_encoded = le.fit_transform(y)\n",
+ "y_tensor = torch.tensor(y_encoded)\n",
+ "\n",
+ "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.4, stratify=y_tensor, random_state=42)\n",
+ "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f265aea3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 32\n",
+ "\n",
+ "batch_size = 32\n",
+ "\n",
+ "X_augmented, y_augmented = augment_rotations(X_train, y_train)\n",
+ "\n",
+ "# Combine original and augmented data\n",
+ "X_train_combined = torch.cat([X_train, X_augmented])\n",
+ "y_train_combined = torch.cat([y_train, y_augmented])\n",
+ "\n",
+ "\n",
+ "train_dataset = TensorDataset(X_train_combined, y_train_combined)\n",
+ "val_dataset = TensorDataset(X_val, y_val)\n",
+ "test_dataset = TensorDataset(X_test, y_test)\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c469bc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
+ "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
+ "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02440bb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_efficientnet_model(num_classes):\n",
+ " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
+ " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a516f06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"Using MPS (Apple GPU)\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print(\"MPS not available. Using CPU\")\n",
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "criterion = nn.CrossEntropyLoss()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "245a6709",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "best_val_acc = 0.0\n",
+ "train_losses = []\n",
+ "val_losses = []\n",
+ "train_accs = []\n",
+ "val_accs = []\n",
+ "epochs_no_improve = 0\n",
+ "early_stop = False\n",
+ "patience = 6\n",
+ "model_name = \"models/best_model_onion_v1.pth\"\n",
+ "\n",
+ "\n",
+ "for epoch in range(30):\n",
+ " if early_stop:\n",
+ " print(f\"Early stopping at epoch {epoch}\")\n",
+ " break\n",
+ " model.train()\n",
+ " total_train_loss = 0\n",
+ " train_correct = 0\n",
+ " train_total = 0\n",
+ "\n",
+ " for batch_x, batch_y in train_loader:\n",
+ " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
+ " preds = model(batch_x)\n",
+ " loss = criterion(preds, batch_y)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " total_train_loss += loss.item()\n",
+ " pred_labels = preds.argmax(dim=1)\n",
+ " train_correct += (pred_labels == batch_y).sum().item()\n",
+ " train_total += batch_y.size(0)\n",
+ "\n",
+ " train_accuracy = train_correct / train_total\n",
+ " avg_train_loss = total_train_loss / len(train_loader)\n",
+ " train_losses.append(avg_train_loss)\n",
+ " train_accs.append(train_accuracy)\n",
+ "\n",
+ " \n",
+ " model.eval()\n",
+ " val_correct = val_total = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_x, val_y in val_loader:\n",
+ " val_x, val_y = val_x.to(device), val_y.to(device)\n",
+ " val_preds = model(val_x).argmax(dim=1)\n",
+ " val_correct += (val_preds == val_y).sum().item()\n",
+ " val_total += val_y.size(0)\n",
+ "\n",
+ " val_accuracy = val_correct / val_total\n",
+ " validation_loss = criterion(model(val_x), val_y).item()\n",
+ "\n",
+ " val_losses.append(validation_loss)\n",
+ " val_accs.append(val_accuracy)\n",
+ "\n",
+ " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
+ " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
+ " if val_accuracy > best_val_acc:\n",
+ " best_val_acc = val_accuracy\n",
+ " torch.save(model.state_dict(), model_name)\n",
+ " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
+ " epochs_no_improve = 0\n",
+ " else:\n",
+ " epochs_no_improve += 1\n",
+ " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
+ "\n",
+ " if epochs_no_improve >= patience:\n",
+ " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
+ " early_stop = True\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bbab1d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epochs = range(1, len(train_losses) + 1)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
+ "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
+ "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930d22bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "model.load_state_dict(torch.load(model_name))\n",
+ "model.eval() \n",
+ "\n",
+ "all_preds = []\n",
+ "all_targets = []\n",
+ "all_images = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch_x, batch_y in test_loader:\n",
+ " batch_x = batch_x.to(device)\n",
+ " preds = model(batch_x).argmax(dim=1).cpu()\n",
+ " all_preds.extend(preds.numpy())\n",
+ " all_targets.extend(batch_y.numpy())\n",
+ " all_images.extend(batch_x.cpu())\n",
+ "\n",
+ "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
+ "test_total = len(all_targets)\n",
+ "test_accuracy = test_correct / test_total\n",
+ "\n",
+ "print(f\"\\nTest Accuracy: {test_accuracy:.4f}\")\n",
+ "\n",
+ "target_names = le.classes_\n",
+ "print(\"\\nClassification Report:\\n\")\n",
+ "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
+ "\n",
+ "cm = confusion_matrix(all_targets, all_preds)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
+ "plt.xlabel(\"Predicted Label\")\n",
+ "plt.ylabel(\"True Label\")\n",
+ "plt.title(\"Confusion Matrix\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4823498a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_preds = np.array(all_preds)\n",
+ "all_targets = np.array(all_targets)\n",
+ "all_images = torch.stack(all_images)\n",
+ "\n",
+ "for class_idx, class_name in enumerate(target_names):\n",
+ " print(f\"\\nShowing False Negatives and False Positives for class: {class_name}\")\n",
+ "\n",
+ " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
+ " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
+ "\n",
+ " def show_images(indices, title, max_images=5):\n",
+ " num = min(len(indices), max_images)\n",
+ " if num == 0:\n",
+ " print(f\"No {title} samples.\")\n",
+ " return\n",
+ "\n",
+ " plt.figure(figsize=(12, 2))\n",
+ " for i, idx in enumerate(indices[:num]):\n",
+ " img = all_images[idx]\n",
+ " img = img.permute(1, 2, 0).numpy()\n",
+ " plt.subplot(1, num, i + 1)\n",
+ " plt.imshow((img - img.min()) / (img.max() - img.min()))\n",
+ " plt.axis('off')\n",
+ " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
+ " plt.suptitle(f\"{title} for {class_name}\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " show_images(fn_indices, \"False Negatives\")\n",
+ " show_images(fp_indices, \"False Positives\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "551cec6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " # Register hooks for all layers in model.features\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0)) # Add batch dimension: [1, 3, 224, 224]\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # Shape: [C, H, W]\n",
+ "\n",
+ " # Compute mean activation per channel\n",
+ " channel_scores = fmap.mean(dim=(1, 2)) # [C]\n",
+ "\n",
+ " # Get indices of top-k channels\n",
+ " topk = torch.topk(channel_scores, k=min(max_channels, fmap.shape[0]))\n",
+ " top_indices = topk.indices\n",
+ "\n",
+ " # Plot top-k channels\n",
+ " plt.figure(figsize=(max_channels * 2, 2.5))\n",
+ " for idx, ch in enumerate(top_indices):\n",
+ " plt.subplot(1, max_channels, idx + 1)\n",
+ " plt.imshow(fmap[ch], cmap='viridis')\n",
+ " plt.title(f\"{layer_name}\\nch{ch.item()} ({channel_scores[ch]:.2f})\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a634b7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3)\n",
+ "model.load_state_dict(torch.load(model_name))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "441d1e09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Onion_512/Whole/image_0001.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "afe5bb8f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Onion_512/Halved/image_0880.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a39585db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Onion_512/Sliced/image_0772.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4aab21c8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/CV/script_pear.ipynb b/scripts/CV/script_pear.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ea160869a4ae424637be2a1a17faf9e6948993f0
--- /dev/null
+++ b/scripts/CV/script_pear.ipynb
@@ -0,0 +1,629 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b70151d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "import seaborn as sns\n",
+ "import torchvision.models as models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c37cc27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
+ " images = []\n",
+ " for root, _, files in os.walk(folder_path):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " try:\n",
+ " img_path = os.path.join(root, file)\n",
+ " img = Image.open(img_path).convert(\"RGB\")\n",
+ " img = img.resize(image_size)\n",
+ " images.append(np.array(img))\n",
+ " except Exception as e:\n",
+ " print(f\"Failed on {img_path}: {e}\")\n",
+ " return np.array(images)\n",
+ "\n",
+ "def plot_rgb_histogram_subplot(ax, images, class_name):\n",
+ " sample = images[random.randint(0, len(images) - 1)]\n",
+ " colors = ('r', 'g', 'b')\n",
+ " for i, col in enumerate(colors):\n",
+ " hist = np.histogram(sample[:, :, i], bins=256, range=(0, 256))[0]\n",
+ " ax.plot(hist, color=col)\n",
+ " ax.set_title(f\"RGB Histogram β {class_name.capitalize()}\")\n",
+ " ax.set_xlabel(\"Pixel Value\")\n",
+ " ax.set_ylabel(\"Frequency\")\n",
+ "\n",
+ "def augment_rotations(X, y):\n",
+ " X_aug = []\n",
+ " y_aug = []\n",
+ " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
+ " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
+ " X_aug.append(X_rot)\n",
+ " y_aug.append(y.clone()) # Same labels for rotated images\n",
+ " return torch.cat(X_aug), torch.cat(y_aug)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f833049",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pear_halved = \"dataset/Pear_512/Halved\"\n",
+ "pear_sliced = \"dataset/Pear_512/Sliced\"\n",
+ "pear_whole = \"dataset/Pear_512/Whole\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e913838",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pear_halved_images = load_images_from_folder(pear_halved)\n",
+ "pear_sliced_images = load_images_from_folder(pear_sliced)\n",
+ "pear_whole_images = load_images_from_folder(pear_whole)\n",
+ "\n",
+ "print(\"pear halved images:\", pear_halved_images.shape)\n",
+ "print(\"pear sliced images:\", pear_sliced_images.shape)\n",
+ "print(\"pear whole images:\", pear_whole_images.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00149f35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "datasets = {\n",
+ " \"halved\": pear_halved_images,\n",
+ " \"sliced\": pear_sliced_images,\n",
+ " \"whole\": pear_whole_images\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def show_random_samples(images, class_name, count=5):\n",
+ " indices = random.sample(range(images.shape[0]), count)\n",
+ " selected = images[indices]\n",
+ "\n",
+ " plt.figure(figsize=(10, 2))\n",
+ " for i, img in enumerate(selected):\n",
+ " plt.subplot(1, count, i+1)\n",
+ " plt.imshow(img.astype(np.uint8))\n",
+ " plt.axis('off')\n",
+ " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "# Display for each class\n",
+ "for class_name, image_array in datasets.items():\n",
+ " show_random_samples(image_array, class_name)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78b16392",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, len(datasets), figsize=(20, 5))\n",
+ "\n",
+ "for ax, (class_name, images) in zip(axes, datasets.items()):\n",
+ " plot_rgb_histogram_subplot(ax, images, class_name)\n",
+ " ax.label_outer() # Hide x labels and tick labels for inner plots\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "856cb530",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = list(datasets.keys())\n",
+ "num_classes = len(class_names)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, num_classes, figsize=(4 * num_classes, 4)) # 1 row, 4 columns\n",
+ "\n",
+ "for i, (class_name, images) in enumerate(datasets.items()):\n",
+ " avg_img = np.mean(images.astype(np.float32), axis=0)\n",
+ " axes[i].imshow(avg_img.astype(np.uint8))\n",
+ " axes[i].set_title(f\"Average Image β {class_name.capitalize()}\")\n",
+ " axes[i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dec6064b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "datasets = {\n",
+ " \"halved\": pear_halved_images,\n",
+ " \"sliced\": pear_sliced_images,\n",
+ " \"whole\": pear_whole_images\n",
+ "}\n",
+ "\n",
+ "# Combine data\n",
+ "X = np.concatenate([pear_halved_images, pear_sliced_images, pear_whole_images], axis=0)\n",
+ "y = (\n",
+ " ['halved'] * len(pear_halved_images) +\n",
+ " ['sliced'] * len(pear_sliced_images) +\n",
+ " ['whole'] * len(pear_whole_images)\n",
+ ")\n",
+ "\n",
+ "# Normalize and convert to torch tensors\n",
+ "X = X.astype(np.float32) / 255.0\n",
+ "X = np.transpose(X, (0, 3, 1, 2)) # (N, C, H, W)\n",
+ "X_tensor = torch.tensor(X)\n",
+ "\n",
+ "# Encode labels\n",
+ "le = LabelEncoder()\n",
+ "y_encoded = le.fit_transform(y)\n",
+ "y_tensor = torch.tensor(y_encoded)\n",
+ "\n",
+ "# Train/val/test split\n",
+ "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.4, stratify=y_tensor, random_state=42)\n",
+ "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f265aea3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "train_dataset = TensorDataset(X_train, y_train)\n",
+ "val_dataset = TensorDataset(X_val, y_val)\n",
+ "test_dataset = TensorDataset(X_test, y_test)\n",
+ "\n",
+ "# DataLoaders\n",
+ "batch_size = 32\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c469bc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
+ "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
+ "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02440bb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torchvision.models as models\n",
+ "\n",
+ "def get_efficientnet_model(num_classes):\n",
+ " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
+ "\n",
+ " # Replace classifier head with custom head\n",
+ " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
+ "\n",
+ " return model\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a516f06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"β Using MPS (Apple GPU)\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print(\"β οΈ MPS not available. Using CPU\")\n",
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "criterion = nn.CrossEntropyLoss()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "245a6709",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "best_val_acc = 0.0\n",
+ "train_losses = []\n",
+ "val_losses = []\n",
+ "train_accs = []\n",
+ "val_accs = []\n",
+ "epochs_no_improve = 0\n",
+ "early_stop = False\n",
+ "patience = 3\n",
+ "model_path = 'models/best_model_pear_v1.pth'\n",
+ "for epoch in range(10):\n",
+ " if early_stop:\n",
+ " print(f\"Early stopping at epoch {epoch}\")\n",
+ " break\n",
+ " model.train()\n",
+ " total_train_loss = 0\n",
+ " train_correct = 0\n",
+ " train_total = 0\n",
+ "\n",
+ " for batch_x, batch_y in train_loader:\n",
+ " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
+ " preds = model(batch_x)\n",
+ " loss = criterion(preds, batch_y)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " total_train_loss += loss.item()\n",
+ "\n",
+ " # Track training accuracy\n",
+ " pred_labels = preds.argmax(dim=1)\n",
+ " train_correct += (pred_labels == batch_y).sum().item()\n",
+ " train_total += batch_y.size(0)\n",
+ "\n",
+ " train_accuracy = train_correct / train_total\n",
+ " avg_train_loss = total_train_loss / len(train_loader)\n",
+ " train_losses.append(avg_train_loss)\n",
+ " train_accs.append(train_accuracy)\n",
+ "\n",
+ " \n",
+ " model.eval()\n",
+ " val_correct = val_total = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_x, val_y in val_loader:\n",
+ " val_x, val_y = val_x.to(device), val_y.to(device)\n",
+ " val_preds = model(val_x).argmax(dim=1)\n",
+ " val_correct += (val_preds == val_y).sum().item()\n",
+ " val_total += val_y.size(0)\n",
+ "\n",
+ " val_accuracy = val_correct / val_total\n",
+ " validation_loss = criterion(model(val_x), val_y).item()\n",
+ "\n",
+ " val_losses.append(validation_loss)\n",
+ " val_accs.append(val_accuracy)\n",
+ "\n",
+ " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
+ " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
+ " if val_accuracy > best_val_acc:\n",
+ " best_val_acc = val_accuracy\n",
+ " torch.save(model.state_dict(), model_path)\n",
+ " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
+ " epochs_no_improve = 0\n",
+ " else:\n",
+ " epochs_no_improve += 1\n",
+ " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
+ "\n",
+ " if epochs_no_improve >= patience:\n",
+ " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
+ " early_stop = True\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bbab1d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epochs = range(1, len(train_losses) + 1)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "# Plot Loss\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
+ "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "# Plot Accuracy\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
+ "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930d22bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "model.load_state_dict(torch.load(model_path))\n",
+ "model.eval() \n",
+ "\n",
+ "all_preds = []\n",
+ "all_targets = []\n",
+ "all_images = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch_x, batch_y in test_loader:\n",
+ " batch_x = batch_x.to(device)\n",
+ " preds = model(batch_x).argmax(dim=1).cpu()\n",
+ " all_preds.extend(preds.numpy())\n",
+ " all_targets.extend(batch_y.numpy())\n",
+ " all_images.extend(batch_x.cpu())\n",
+ "\n",
+ "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
+ "test_total = len(all_targets)\n",
+ "test_accuracy = test_correct / test_total\n",
+ "\n",
+ "print(f\"\\nTest Accuracy: {test_accuracy:.4f}\")\n",
+ "\n",
+ "target_names = le.classes_\n",
+ "print(\"\\nClassification Report:\\n\")\n",
+ "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
+ "\n",
+ "cm = confusion_matrix(all_targets, all_preds)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
+ "plt.xlabel(\"Predicted Label\")\n",
+ "plt.ylabel(\"True Label\")\n",
+ "plt.title(\"Confusion Matrix\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4823498a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_preds = np.array(all_preds)\n",
+ "all_targets = np.array(all_targets)\n",
+ "all_images = torch.stack(all_images)\n",
+ "\n",
+ "for class_idx, class_name in enumerate(target_names):\n",
+ " print(f\"\\nShowing False Negatives and False Positives for class: {class_name}\")\n",
+ "\n",
+ " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
+ " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
+ "\n",
+ " def show_images(indices, title, max_images=5):\n",
+ " num = min(len(indices), max_images)\n",
+ " if num == 0:\n",
+ " print(f\"No {title} samples.\")\n",
+ " return\n",
+ "\n",
+ " plt.figure(figsize=(12, 2))\n",
+ " for i, idx in enumerate(indices[:num]):\n",
+ " img = all_images[idx]\n",
+ " img = img.permute(1, 2, 0).numpy()\n",
+ " plt.subplot(1, num, i + 1)\n",
+ " plt.imshow((img - img.min()) / (img.max() - img.min()))\n",
+ " plt.axis('off')\n",
+ " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
+ " plt.suptitle(f\"{title} for {class_name}\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " show_images(fn_indices, \"False Negatives\")\n",
+ " show_images(fp_indices, \"False Positives\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "551cec6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " # Register hooks for all layers in model.features\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0)) # Add batch dimension: [1, 3, 224, 224]\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # Shape: [C, H, W]\n",
+ "\n",
+ " # Compute mean activation per channel\n",
+ " channel_scores = fmap.mean(dim=(1, 2)) # [C]\n",
+ "\n",
+ " # Get indices of top-k channels\n",
+ " topk = torch.topk(channel_scores, k=min(max_channels, fmap.shape[0]))\n",
+ " top_indices = topk.indices\n",
+ "\n",
+ " # Plot top-k channels\n",
+ " plt.figure(figsize=(max_channels * 2, 2.5))\n",
+ " for idx, ch in enumerate(top_indices):\n",
+ " plt.subplot(1, max_channels, idx + 1)\n",
+ " plt.imshow(fmap[ch], cmap='viridis')\n",
+ " plt.title(f\"{layer_name}\\nch{ch.item()} ({channel_scores[ch]:.2f})\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e600a55d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3)\n",
+ "model.load_state_dict(torch.load(model_path))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c23701f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Pear_512/Whole/image_0007.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26891996",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Pear_512/Halved/image_0578.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "798dcfa3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Pear_512/Sliced/image_0007.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "337ca29d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/CV/script_strawberry.ipynb b/scripts/CV/script_strawberry.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f9d4ac5b46cee636bc5e9964ced626bdc7ed484c
--- /dev/null
+++ b/scripts/CV/script_strawberry.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b70151d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "import seaborn as sns\n",
+ "import torchvision.models as models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c37cc27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
+ " images = []\n",
+ " for root, _, files in os.walk(folder_path):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " try:\n",
+ " img_path = os.path.join(root, file)\n",
+ " img = Image.open(img_path).convert(\"RGB\")\n",
+ " img = img.resize(image_size)\n",
+ " images.append(np.array(img))\n",
+ " except Exception as e:\n",
+ " print(f\"Failed on {img_path}: {e}\")\n",
+ " return np.array(images)\n",
+ "\n",
+ "def plot_rgb_histogram_subplot(ax, images, class_name):\n",
+ " sample = images[random.randint(0, len(images) - 1)]\n",
+ " colors = ('r', 'g', 'b')\n",
+ " for i, col in enumerate(colors):\n",
+ " hist = np.histogram(sample[:, :, i], bins=256, range=(0, 256))[0]\n",
+ " ax.plot(hist, color=col)\n",
+ " ax.set_title(f\"RGB Histogram β {class_name.capitalize()}\")\n",
+ " ax.set_xlabel(\"Pixel Value\")\n",
+ " ax.set_ylabel(\"Frequency\")\n",
+ "\n",
+ "def augment_rotations(X, y):\n",
+ " X_aug = []\n",
+ " y_aug = []\n",
+ " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
+ " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
+ " X_aug.append(X_rot)\n",
+ " y_aug.append(y.clone()) # Same labels for rotated images\n",
+ " return torch.cat(X_aug), torch.cat(y_aug)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f833049",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "strawberry_halved = \"dataset/Strawberry_512/Hulled\"\n",
+ "strawberry_sliced = \"dataset/Strawberry_512/Sliced\"\n",
+ "strawberry_whole = \"dataset/Strawberry_512/Whole\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e913838",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "strawberry_hulled_images = load_images_from_folder(strawberry_halved)\n",
+ "strawberry_sliced_images = load_images_from_folder(strawberry_sliced)\n",
+ "strawberry_whole_images = load_images_from_folder(strawberry_whole)\n",
+ "\n",
+ "print(\"Strawberry halved images:\", strawberry_hulled_images.shape)\n",
+ "print(\"Strawberry sliced images:\", strawberry_sliced_images.shape)\n",
+ "print(\"Strawberry whole images:\", strawberry_whole_images.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00149f35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "datasets = {\n",
+ " \"Hulled\": strawberry_hulled_images,\n",
+ " \"sliced\": strawberry_sliced_images,\n",
+ " \"whole\": strawberry_whole_images\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def show_random_samples(images, class_name, count=5):\n",
+ " indices = random.sample(range(images.shape[0]), count)\n",
+ " selected = images[indices]\n",
+ "\n",
+ " plt.figure(figsize=(10, 2))\n",
+ " for i, img in enumerate(selected):\n",
+ " plt.subplot(1, count, i+1)\n",
+ " plt.imshow(img.astype(np.uint8))\n",
+ " plt.axis('off')\n",
+ " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "# Display for each class\n",
+ "for class_name, image_array in datasets.items():\n",
+ " show_random_samples(image_array, class_name)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a7c574a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, len(datasets), figsize=(20, 5))\n",
+ "\n",
+ "for ax, (class_name, images) in zip(axes, datasets.items()):\n",
+ " plot_rgb_histogram_subplot(ax, images, class_name)\n",
+ " ax.label_outer() # Hide x labels and tick labels for inner plots\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a69bf6f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = list(datasets.keys())\n",
+ "num_classes = len(class_names)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, num_classes, figsize=(4 * num_classes, 4)) # 1 row, 4 columns\n",
+ "\n",
+ "for i, (class_name, images) in enumerate(datasets.items()):\n",
+ " avg_img = np.mean(images.astype(np.float32), axis=0)\n",
+ " axes[i].imshow(avg_img.astype(np.uint8))\n",
+ " axes[i].set_title(f\"Average Image β {class_name.capitalize()}\")\n",
+ " axes[i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dec6064b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datasets = {\n",
+ " \"hulled\": strawberry_hulled_images,\n",
+ " \"sliced\": strawberry_sliced_images,\n",
+ " \"whole\": strawberry_whole_images\n",
+ "}\n",
+ "\n",
+ "# Combine data\n",
+ "X = np.concatenate([strawberry_hulled_images, strawberry_sliced_images, strawberry_whole_images], axis=0)\n",
+ "y = (\n",
+ " ['hulled'] * len(strawberry_hulled_images) +\n",
+ " ['sliced'] * len(strawberry_sliced_images) +\n",
+ " ['whole'] * len(strawberry_whole_images)\n",
+ ")\n",
+ "\n",
+ "# Normalize and convert to torch tensors\n",
+ "X = X.astype(np.float32) / 255.0\n",
+ "X = np.transpose(X, (0, 3, 1, 2)) # (N, C, H, W)\n",
+ "X_tensor = torch.tensor(X)\n",
+ "\n",
+ "# Encode labels\n",
+ "le = LabelEncoder()\n",
+ "y_encoded = le.fit_transform(y)\n",
+ "y_tensor = torch.tensor(y_encoded)\n",
+ "\n",
+ "# Train/val/test split\n",
+ "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.5, stratify=y_tensor, random_state=42)\n",
+ "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f265aea3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 32\n",
+ "\n",
+ "X_augmented, y_augmented = augment_rotations(X_train, y_train)\n",
+ "\n",
+ "# Combine original and augmented data\n",
+ "X_train_combined = torch.cat([X_train, X_augmented])\n",
+ "y_train_combined = torch.cat([y_train, y_augmented])\n",
+ "\n",
+ "\n",
+ "train_dataset = TensorDataset(X_train_combined, y_train_combined)\n",
+ "val_dataset = TensorDataset(X_val, y_val)\n",
+ "test_dataset = TensorDataset(X_test, y_test)\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c469bc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
+ "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
+ "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02440bb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torchvision.models as models\n",
+ "\n",
+ "def get_efficientnet_model(num_classes):\n",
+ " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
+ " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
+ " return model\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a516f06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"Using MPS (Apple GPU)\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print(\"MPS not available. Using CPU\")\n",
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "criterion = nn.CrossEntropyLoss()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "245a6709",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "best_val_acc = 0.0\n",
+ "train_losses = []\n",
+ "val_losses = []\n",
+ "train_accs = []\n",
+ "val_accs = []\n",
+ "epochs_no_improve = 0\n",
+ "early_stop = False\n",
+ "patience = 6\n",
+ "model_name = \"models/best_model_strawberry_v1.pth\"\n",
+ "\n",
+ "for epoch in range(30):\n",
+ " if early_stop:\n",
+ " print(f\"Early stopping at epoch {epoch}\")\n",
+ " break\n",
+ " model.train()\n",
+ " total_train_loss = 0\n",
+ " train_correct = 0\n",
+ " train_total = 0\n",
+ "\n",
+ " for batch_x, batch_y in train_loader:\n",
+ " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
+ " preds = model(batch_x)\n",
+ " loss = criterion(preds, batch_y)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " total_train_loss += loss.item()\n",
+ "\n",
+ " pred_labels = preds.argmax(dim=1)\n",
+ " train_correct += (pred_labels == batch_y).sum().item()\n",
+ " train_total += batch_y.size(0)\n",
+ "\n",
+ " train_accuracy = train_correct / train_total\n",
+ " avg_train_loss = total_train_loss / len(train_loader)\n",
+ " train_losses.append(avg_train_loss)\n",
+ " train_accs.append(train_accuracy)\n",
+ "\n",
+ " \n",
+ " model.eval()\n",
+ " val_correct = val_total = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_x, val_y in val_loader:\n",
+ " val_x, val_y = val_x.to(device), val_y.to(device)\n",
+ " val_preds = model(val_x).argmax(dim=1)\n",
+ " val_correct += (val_preds == val_y).sum().item()\n",
+ " val_total += val_y.size(0)\n",
+ "\n",
+ " val_accuracy = val_correct / val_total\n",
+ " validation_loss = criterion(model(val_x), val_y).item()\n",
+ "\n",
+ " # After calculating val_accuracy\n",
+ " val_losses.append(validation_loss)\n",
+ " val_accs.append(val_accuracy)\n",
+ "\n",
+ " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
+ " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
+ " if val_accuracy > best_val_acc:\n",
+ " best_val_acc = val_accuracy\n",
+ " torch.save(model.state_dict(), model_name)\n",
+ " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
+ " epochs_no_improve = 0\n",
+ " else:\n",
+ " epochs_no_improve += 1\n",
+ " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
+ "\n",
+ " if epochs_no_improve >= patience:\n",
+ " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
+ " early_stop = True\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bbab1d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "epochs = range(1, len(train_losses) + 1)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
+ "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
+ "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930d22bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "model.load_state_dict(torch.load(model_name))\n",
+ "model.eval() \n",
+ "\n",
+ "all_preds = []\n",
+ "all_targets = []\n",
+ "all_images = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch_x, batch_y in test_loader:\n",
+ " batch_x = batch_x.to(device)\n",
+ " preds = model(batch_x).argmax(dim=1).cpu()\n",
+ " all_preds.extend(preds.numpy())\n",
+ " all_targets.extend(batch_y.numpy())\n",
+ " all_images.extend(batch_x.cpu())\n",
+ "\n",
+ "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
+ "test_total = len(all_targets)\n",
+ "test_accuracy = test_correct / test_total\n",
+ "\n",
+ "print(f\"\\nTest Accuracy: {test_accuracy:.4f}\")\n",
+ "\n",
+ "target_names = le.classes_\n",
+ "print(\"\\nClassification Report:\\n\")\n",
+ "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
+ "\n",
+ "cm = confusion_matrix(all_targets, all_preds)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
+ "plt.xlabel(\"Predicted Label\")\n",
+ "plt.ylabel(\"True Label\")\n",
+ "plt.title(\"Confusion Matrix\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4823498a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_preds = np.array(all_preds)\n",
+ "all_targets = np.array(all_targets)\n",
+ "all_images = torch.stack(all_images) # shape: [N, C, H, W]\n",
+ "\n",
+ "# Per class FP and FN\n",
+ "for class_idx, class_name in enumerate(target_names):\n",
+ " print(f\"\\nπ Showing False Negatives and False Positives for class: {class_name}\")\n",
+ "\n",
+ " # False Negatives: True label is class_idx, but predicted something else\n",
+ " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
+ " # False Positives: Predicted class_idx, but true label is different\n",
+ " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
+ "\n",
+ " def show_images(indices, title, max_images=5):\n",
+ " num = min(len(indices), max_images)\n",
+ " if num == 0:\n",
+ " print(f\"β No {title} samples.\")\n",
+ " return\n",
+ "\n",
+ " plt.figure(figsize=(12, 2))\n",
+ " for i, idx in enumerate(indices[:num]):\n",
+ " img = all_images[idx]\n",
+ " img = img.permute(1, 2, 0).numpy() # [C, H, W] β [H, W, C]\n",
+ " plt.subplot(1, num, i + 1)\n",
+ " plt.imshow((img - img.min()) / (img.max() - img.min())) # normalize to [0,1] for display\n",
+ " plt.axis('off')\n",
+ " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
+ " plt.suptitle(f\"{title} for {class_name}\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " show_images(fn_indices, \"False Negatives\")\n",
+ " show_images(fp_indices, \"False Positives\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "551cec6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " # Register hooks for all layers in model.features\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0)) # Add batch dimension: [1, 3, 224, 224]\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # Shape: [C, H, W]\n",
+ "\n",
+ " # Compute mean activation per channel\n",
+ " channel_scores = fmap.mean(dim=(1, 2)) # [C]\n",
+ "\n",
+ " # Get indices of top-k channels\n",
+ " topk = torch.topk(channel_scores, k=min(max_channels, fmap.shape[0]))\n",
+ " top_indices = topk.indices\n",
+ "\n",
+ " # Plot top-k channels\n",
+ " plt.figure(figsize=(max_channels * 2, 2.5))\n",
+ " for idx, ch in enumerate(top_indices):\n",
+ " plt.subplot(1, max_channels, idx + 1)\n",
+ " plt.imshow(fmap[ch], cmap='viridis')\n",
+ " plt.title(f\"{layer_name}\\nch{ch.item()} ({channel_scores[ch]:.2f})\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a0c0cdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3)\n",
+ "model.load_state_dict(torch.load(model_name))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71770f98",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Strawberry_512/Whole/image_0017.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fda0bcc9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Strawberry_512/Hulled/image_0001.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4334ee87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Strawberry_512/Sliced/image_0001.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e261e5f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/CV/script_tomato.ipynb b/scripts/CV/script_tomato.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dd9d11a4483d6fc1857ef018af69d8bfe94621e9
--- /dev/null
+++ b/scripts/CV/script_tomato.ipynb
@@ -0,0 +1,656 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b70151d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "import seaborn as sns\n",
+ "import torchvision.models as models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c37cc27b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_images_from_folder(folder_path, image_size=(224, 224)):\n",
+ " images = []\n",
+ " for root, _, files in os.walk(folder_path):\n",
+ " for file in files:\n",
+ " if file.lower().endswith((\".jpg\", \".jpeg\")):\n",
+ " try:\n",
+ " img_path = os.path.join(root, file)\n",
+ " img = Image.open(img_path).convert(\"RGB\")\n",
+ " img = img.resize(image_size)\n",
+ " images.append(np.array(img))\n",
+ " except Exception as e:\n",
+ " print(f\"Failed on {img_path}: {e}\")\n",
+ " return np.array(images)\n",
+ "\n",
+ "def plot_rgb_histogram_subplot(ax, images, class_name):\n",
+ " sample = images[random.randint(0, len(images) - 1)]\n",
+ " colors = ('r', 'g', 'b')\n",
+ " for i, col in enumerate(colors):\n",
+ " hist = np.histogram(sample[:, :, i], bins=256, range=(0, 256))[0]\n",
+ " ax.plot(hist, color=col)\n",
+ " ax.set_title(f\"RGB Histogram β {class_name.capitalize()}\")\n",
+ " ax.set_xlabel(\"Pixel Value\")\n",
+ " ax.set_ylabel(\"Frequency\")\n",
+ "\n",
+ "def augment_rotations(X, y):\n",
+ " X_aug = []\n",
+ " y_aug = []\n",
+ " for k in [1, 2, 3]: # 90, 180, 270 degrees\n",
+ " X_rot = torch.rot90(X, k=k, dims=[2, 3]) # rotate along H and W\n",
+ " X_aug.append(X_rot)\n",
+ " y_aug.append(y.clone()) # Same labels for rotated images\n",
+ " return torch.cat(X_aug), torch.cat(y_aug)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f833049",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tomato_diced = \"dataset/Tomato_512/Diced\"\n",
+ "tomato_vines = \"dataset/Tomato_512/On_the_vines\"\n",
+ "tomato_whole = \"dataset/Tomato_512/Whole\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e913838",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "tomato_diced_images = load_images_from_folder(tomato_diced)\n",
+ "tomato_vines_images = load_images_from_folder(tomato_vines)\n",
+ "tomato_whole_images = load_images_from_folder(tomato_whole)\n",
+ "\n",
+ "print(\"Strawberry halved images:\", tomato_diced_images.shape)\n",
+ "print(\"Strawberry sliced images:\", tomato_vines_images.shape)\n",
+ "print(\"Strawberry whole images:\", tomato_whole_images.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00149f35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "datasets = {\n",
+ " \"diced\": tomato_diced_images,\n",
+ " \"vines\": tomato_vines_images,\n",
+ " \"whole\": tomato_whole_images\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def show_random_samples(images, class_name, count=5):\n",
+ " indices = random.sample(range(images.shape[0]), count)\n",
+ " selected = images[indices]\n",
+ "\n",
+ " plt.figure(figsize=(10, 2))\n",
+ " for i, img in enumerate(selected):\n",
+ " plt.subplot(1, count, i+1)\n",
+ " plt.imshow(img.astype(np.uint8))\n",
+ " plt.axis('off')\n",
+ " plt.suptitle(f\"{class_name.capitalize()} β Random {count} Samples\", fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "# Display for each class\n",
+ "for class_name, image_array in datasets.items():\n",
+ " show_random_samples(image_array, class_name)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "46a700fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, len(datasets), figsize=(20, 5))\n",
+ "\n",
+ "for ax, (class_name, images) in zip(axes, datasets.items()):\n",
+ " plot_rgb_histogram_subplot(ax, images, class_name)\n",
+ " ax.label_outer() # Hide x labels and tick labels for inner plots\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b0c6d31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = list(datasets.keys())\n",
+ "num_classes = len(class_names)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, num_classes, figsize=(4 * num_classes, 4)) # 1 row, 4 columns\n",
+ "\n",
+ "for i, (class_name, images) in enumerate(datasets.items()):\n",
+ " avg_img = np.mean(images.astype(np.float32), axis=0)\n",
+ " axes[i].imshow(avg_img.astype(np.uint8))\n",
+ " axes[i].set_title(f\"Average Image β {class_name.capitalize()}\")\n",
+ " axes[i].axis('off')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dec6064b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from torchvision import transforms\n",
+ "\n",
+ "datasets = {\n",
+ " \"diced\": tomato_diced_images,\n",
+ " \"vines\": tomato_vines_images,\n",
+ " \"whole\": tomato_whole_images\n",
+ "}\n",
+ "\n",
+ "# Combine data\n",
+ "X = np.concatenate([tomato_diced_images, tomato_vines_images, tomato_whole_images], axis=0)\n",
+ "y = (\n",
+ " ['diced'] * len(tomato_diced_images) +\n",
+ " ['vines'] * len(tomato_vines_images) +\n",
+ " ['whole'] * len(tomato_whole_images)\n",
+ ")\n",
+ "\n",
+ "# Normalize and convert to torch tensors\n",
+ "X = X.astype(np.float32) / 255.0\n",
+ "X = np.transpose(X, (0, 3, 1, 2)) # (N, C, H, W)\n",
+ "X_tensor = torch.tensor(X)\n",
+ "\n",
+ "# Encode labels\n",
+ "le = LabelEncoder()\n",
+ "y_encoded = le.fit_transform(y)\n",
+ "y_tensor = torch.tensor(y_encoded)\n",
+ "\n",
+ "# Train/val/test split\n",
+ "X_train, X_temp, y_train, y_temp = train_test_split(X_tensor, y_tensor, test_size=0.4, stratify=y_tensor, random_state=42)\n",
+ "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f265aea3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 32\n",
+ "\n",
+ "X_augmented, y_augmented = augment_rotations(X_train, y_train)\n",
+ "\n",
+ "# Combine original and augmented data\n",
+ "X_train_combined = torch.cat([X_train, X_augmented])\n",
+ "y_train_combined = torch.cat([y_train, y_augmented])\n",
+ "\n",
+ "# Create new training dataset and loader\n",
+ "\n",
+ "train_dataset = TensorDataset(X_train, y_train)\n",
+ "val_dataset = TensorDataset(X_val, y_val)\n",
+ "test_dataset = TensorDataset(X_test, y_test)\n",
+ "\n",
+ "# DataLoaders\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c469bc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"π’ Train Dataset: {len(train_dataset)} samples, {len(train_loader)} batches\")\n",
+ "print(f\"π’ Val Dataset: {len(val_dataset)} samples, {len(val_loader)} batches\")\n",
+ "print(f\"π’ Test Dataset: {len(test_dataset)} samples, {len(test_loader)} batches\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02440bb8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torchvision.models as models\n",
+ "\n",
+ "def get_efficientnet_model(num_classes):\n",
+ " model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)\n",
+ "\n",
+ " # Replace classifier head with custom head\n",
+ " model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)\n",
+ "\n",
+ " return model\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a516f06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = torch.device(\"mps\")\n",
+ " print(\"β Using MPS (Apple GPU)\")\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print(\"β οΈ MPS not available. Using CPU\")\n",
+ "\n",
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
+ "criterion = nn.CrossEntropyLoss()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "245a6709",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "best_val_acc = 0.0\n",
+ "train_losses = []\n",
+ "val_losses = []\n",
+ "train_accs = []\n",
+ "val_accs = []\n",
+ "epochs_no_improve = 0\n",
+ "early_stop = False\n",
+ "patience = 3\n",
+ "\n",
+ "for epoch in range(10):\n",
+ " if early_stop:\n",
+ " print(f\"Early stopping at epoch {epoch}\")\n",
+ " break\n",
+ " model.train()\n",
+ " total_train_loss = 0\n",
+ " train_correct = 0\n",
+ " train_total = 0\n",
+ "\n",
+ " for batch_x, batch_y in train_loader:\n",
+ " batch_x, batch_y = batch_x.to(device), batch_y.to(device)\n",
+ " preds = model(batch_x)\n",
+ " loss = criterion(preds, batch_y)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " total_train_loss += loss.item()\n",
+ "\n",
+ " # Track training accuracy\n",
+ " pred_labels = preds.argmax(dim=1)\n",
+ " train_correct += (pred_labels == batch_y).sum().item()\n",
+ " train_total += batch_y.size(0)\n",
+ "\n",
+ " train_accuracy = train_correct / train_total\n",
+ " avg_train_loss = total_train_loss / len(train_loader)\n",
+ " train_losses.append(avg_train_loss)\n",
+ " train_accs.append(train_accuracy)\n",
+ "\n",
+ " \n",
+ " model.eval()\n",
+ " val_correct = val_total = 0\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for val_x, val_y in val_loader:\n",
+ " val_x, val_y = val_x.to(device), val_y.to(device)\n",
+ " val_preds = model(val_x).argmax(dim=1)\n",
+ " val_correct += (val_preds == val_y).sum().item()\n",
+ " val_total += val_y.size(0)\n",
+ "\n",
+ " val_accuracy = val_correct / val_total\n",
+ " validation_loss = criterion(model(val_x), val_y).item()\n",
+ "\n",
+ " # After calculating val_accuracy\n",
+ " val_losses.append(validation_loss)\n",
+ " val_accs.append(val_accuracy)\n",
+ "\n",
+ " print(f\"Epoch {epoch+1:02d} | Train Loss: {avg_train_loss:.4f} | \"\n",
+ " f\"Train Acc: {train_accuracy:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
+ " if val_accuracy > best_val_acc:\n",
+ " best_val_acc = val_accuracy\n",
+ " torch.save(model.state_dict(), \"best_model_tomato_v1.pth\")\n",
+ " print(f\"New best model saved at epoch {epoch+1} with val acc {val_accuracy:.4f}\")\n",
+ " epochs_no_improve = 0\n",
+ " else:\n",
+ " epochs_no_improve += 1\n",
+ " print(f\"No improvement for {epochs_no_improve} epoch(s)\")\n",
+ "\n",
+ " if epochs_no_improve >= patience:\n",
+ " print(f\"Validation accuracy did not improve for {patience} consecutive epochs. Stopping early.\")\n",
+ " early_stop = True\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bbab1d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "epochs = range(1, len(train_losses) + 1)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "# Plot Loss\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot(epochs, train_losses, label='Train Loss', marker='o')\n",
+ "plt.plot(epochs, val_losses, label='Validation Loss', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.title('Loss per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "# Plot Accuracy\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot(epochs, train_accs, label='Train Accuracy', marker='o')\n",
+ "plt.plot(epochs, val_accs, label='Validation Accuracy', marker='s')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.title('Accuracy per Epoch')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930d22bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3).to(device)\n",
+ "model.load_state_dict(torch.load(\"models/best_model_tomato_v1.pth\"))\n",
+ "model.eval() \n",
+ "\n",
+ "all_preds = []\n",
+ "all_targets = []\n",
+ "all_images = []\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " for batch_x, batch_y in test_loader:\n",
+ " batch_x = batch_x.to(device)\n",
+ " preds = model(batch_x).argmax(dim=1).cpu()\n",
+ " all_preds.extend(preds.numpy())\n",
+ " all_targets.extend(batch_y.numpy())\n",
+ " all_images.extend(batch_x.cpu())\n",
+ "\n",
+ "test_correct = sum(np.array(all_preds) == np.array(all_targets))\n",
+ "test_total = len(all_targets)\n",
+ "test_accuracy = test_correct / test_total\n",
+ "\n",
+ "print(f\"\\nTest Accuracy: {test_accuracy:.4f}\")\n",
+ "\n",
+ "target_names = le.classes_\n",
+ "print(\"\\nClassification Report:\\n\")\n",
+ "print(classification_report(all_targets, all_preds, target_names=target_names))\n",
+ "\n",
+ "cm = confusion_matrix(all_targets, all_preds)\n",
+ "\n",
+ "plt.figure(figsize=(6, 5))\n",
+ "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
+ "plt.xlabel(\"Predicted Label\")\n",
+ "plt.ylabel(\"True Label\")\n",
+ "plt.title(\"Confusion Matrix\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4823498a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "all_preds = np.array(all_preds)\n",
+ "all_targets = np.array(all_targets)\n",
+ "all_images = torch.stack(all_images) # shape: [N, C, H, W]\n",
+ "\n",
+ "# Per class FP and FN\n",
+ "for class_idx, class_name in enumerate(target_names):\n",
+ " print(f\"\\nπ Showing False Negatives and False Positives for class: {class_name}\")\n",
+ "\n",
+ " # False Negatives: True label is class_idx, but predicted something else\n",
+ " fn_indices = np.where((all_targets == class_idx) & (all_preds != class_idx))[0]\n",
+ " # False Positives: Predicted class_idx, but true label is different\n",
+ " fp_indices = np.where((all_preds == class_idx) & (all_targets != class_idx))[0]\n",
+ "\n",
+ " def show_images(indices, title, max_images=5):\n",
+ " num = min(len(indices), max_images)\n",
+ " if num == 0:\n",
+ " print(f\"β No {title} samples.\")\n",
+ " return\n",
+ "\n",
+ " plt.figure(figsize=(12, 2))\n",
+ " for i, idx in enumerate(indices[:num]):\n",
+ " img = all_images[idx]\n",
+ " img = img.permute(1, 2, 0).numpy() # [C, H, W] β [H, W, C]\n",
+ " plt.subplot(1, num, i + 1)\n",
+ " plt.imshow((img - img.min()) / (img.max() - img.min())) # normalize to [0,1] for display\n",
+ " plt.axis('off')\n",
+ " plt.title(f\"Pred: {target_names[all_preds[idx]]}\\nTrue: {target_names[all_targets[idx]]}\")\n",
+ " plt.suptitle(f\"{title} for {class_name}\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " show_images(fn_indices, \"False Negatives\")\n",
+ " show_images(fp_indices, \"False Positives\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "551cec6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_channels(model, image_tensor, max_channels=6):\n",
+ " model.eval()\n",
+ " activations = {}\n",
+ "\n",
+ " def get_activation(name):\n",
+ " def hook(model, input, output):\n",
+ " activations[name] = output.detach().cpu()\n",
+ " return hook\n",
+ "\n",
+ " # Register hooks for all layers in model.features\n",
+ " hooks = []\n",
+ " for i in range(len(model.features)):\n",
+ " layer = model.features[i]\n",
+ " hooks.append(layer.register_forward_hook(get_activation(f\"features_{i}\")))\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " _ = model(image_tensor.unsqueeze(0)) # Add batch dimension: [1, 3, 224, 224]\n",
+ "\n",
+ " for h in hooks:\n",
+ " h.remove()\n",
+ "\n",
+ " for layer_name, fmap in activations.items():\n",
+ " fmap = fmap.squeeze(0) # Shape: [C, H, W]\n",
+ "\n",
+ " # Compute mean activation per channel\n",
+ " channel_scores = fmap.mean(dim=(1, 2)) # [C]\n",
+ "\n",
+ " # Get indices of top-k channels\n",
+ " topk = torch.topk(channel_scores, k=min(max_channels, fmap.shape[0]))\n",
+ " top_indices = topk.indices\n",
+ "\n",
+ " # Plot top-k channels\n",
+ " plt.figure(figsize=(max_channels * 2, 2.5))\n",
+ " for idx, ch in enumerate(top_indices):\n",
+ " plt.subplot(1, max_channels, idx + 1)\n",
+ " plt.imshow(fmap[ch], cmap='viridis')\n",
+ " plt.title(f\"{layer_name}\\nch{ch.item()} ({channel_scores[ch]:.2f})\")\n",
+ " plt.axis('off')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b524ecc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_efficientnet_model(num_classes=3)\n",
+ "model.load_state_dict(torch.load(\"models/best_model_tomato_v1.pth\"))\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8c2c9f8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "img = Image.open(\"dataset/Tomato_512/Whole/image_0007.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c382875b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Tomato_512/On_the_vines/image_0578.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c450913",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open(\"dataset/Tomato_512/Diced/image_0578.jpg\").convert(\"RGB\")\n",
+ "\n",
+ "# Preprocessing (must match model requirements)\n",
+ "transform = transforms.Compose([\n",
+ " transforms.Resize((224, 224)),\n",
+ " transforms.ToTensor()\n",
+ "])\n",
+ "img_tensor = transform(img) # shape: [3, 224, 224]\n",
+ "\n",
+ "# Visualize feature maps\n",
+ "visualize_channels(model, img_tensor, max_channels=16)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d54e3240",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.21"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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