Delete SlimFace

SlimFace/.gitignore

@@ -1,197 +0,0 @@
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-*$py.class
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SlimFace/.python-version

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-3.11.11

SlimFace/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 Danh Tran
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

SlimFace/README.md

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-# SlimFace: Slim Face Recognition
-
-> ## Credits and Citation
->
-> ℹ️ This project is based on the [![Built on edgeface](https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github)](https://github.com/otroshi/edgeface) by [![Hatef Otroshi](https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github)](https://github.com/otroshi), and includes our own bug fixes and enhancements.
->
-> If this project is helpful for your research, please consider citing the original paper:
->
-> **Edgeface: Efficient face recognition model for edge devices**  
-> *George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien*  
-> *IEEE Transactions on Biometrics, Behavior, and Identity Science (2024)*
->
-> **If you use this work in your research, please cite the original paper:**
-> ```bibtex
-> @article{edgeface,
->   title={Edgeface: Efficient face recognition model for edge devices},
->   author={George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien},
->   journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
->   year={2024}
-> }
-> ```
-
-
-## Usage
-### Clone Repositories
-```bash
-# Clone the repository
-git clone https://github.com/danhtran2mind/SlimFace
-
-# Navigate into the newly created 'slimface' directory.
-cd SlimFace
-```
-### Install Dependencies
-**If Open-CV (CV2) does not work, run below CLI**
-```bash
-sudo apt update
-sudo apt install -y libglib2.0-0
-sudo apt install -y libgl1-mesa-dev
-```
-### Default install Dependencies
-```bash
-pip install -r requirements/requirements.txt
-```
-### Other install Dependencies
-- For My Compatible
-```bash
-pip install -r requirements/requirements_compatible.txt
-```
-- For `End2end Inference`
-```bash
-pip install -r requirements/requirements_inference.txt
-```
-### Download Model Checkpoints
-```bash
-python scripts/download_ckpts.py
-```
-### Setup Third Party
-```bash
-python scripts/setup_third_party.py
-```
-## Data Preparation
-
-## Pre-trained Model preparation
-For detailed instructions on how to process and manage your data effectively, refer to the [Full guide for data processing](./docs/data_processing.md).
-
-This is fast usage for dataset preparation
-```bash
-python scripts/process_dataset.py
-```
-## Training
-
-1. Configure the default settings for Accelerate:
-```bash
-accelerate config default
-```
-
-2. Launch the training script using Accelerate:
-```bash
-accelerate launch src/slimface/training/accelerate_train.py
-```
-
-For additional help, you can refer to the [Training Documentation](./docs/training/training_docs.md) for more details.
-
-### Inference
-#### Create Reference Images Data at `data/reference_data/images`
-For each class, you store an image in `data/reference_data/images` folder which are maped with `index_to_class_mapping.json`.
-
-The structure like:
-```markdown
-data/reference_data/images/
-├── 'Robert Downey Jr.jpg'
-├── 'Tom Cruise.jpg'
-└── ...
-```
-
-
-### Create Reference Dictionary from `index_to_class_mapping.json`
-
-#### Steps
-1. Place `index_to_class_mapping.json` in the `ckpts` folder.
-2. Ensure reference images are in `data/reference_data/images`. Missing images will be set to `""` in `reference_image_data.json` (default in `data/reference_data` folder).
-3. Run one of the following commands:
-
-#### Commands
-- **Default** (Output: `data/reference_data/reference_image_data.json`):
-  ```bash
-  python scripts/create_reference_image_path.py
-  ```
-- **Custom Paths**:
-  ```bash
-  python scripts/create_reference_image_path.py \
-      --input <path_to_index_to_class_mapping.json> \
-      --output <path_to_tests/reference_image_data.json>
-  ```
-
-#### Manual Option
-Edit `reference_image_data.json` directly to add image paths as dictionary values.
-
-## Demostration
-```bash
-python apps/gradio_app.py
-```
-
-https://huggingface.co/spaces/danhtran2mind/SlimFace-demo
-
-## Project Description
-
-This repository is trained from [![GitHub Repo](https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat)](https://github.com/danhtran2mind/edgeface), a fork of [![GitHub Repo](https://img.shields.io/badge/GitHub-otroshi%2Fedgeface-blue?style=flat)](https://github.com/otroshi/edgeface), with numerous bug fixes and rewritten code for improved performance and stability.

SlimFace/apps/gradio_app.py

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-import gradio as gr
-from PIL import Image
-from gradio_app.inference import run_inference
-from gradio_app.components import (
-    CONTENT_DESCRIPTION, CONTENT_IN, CONTENT_OUT,
-     list_reference_files, list_mapping_files,
-      list_classifier_files, list_edgeface_files
-)
-
-def create_image_input_column():
-    """Create the column for image input and output display."""
-    with gr.Column():
-        image_input = gr.Image(type="pil", label="Upload Image")
-        output = gr.HTML(label="Inference Results", elem_classes=["results-container"])
-    return image_input, output
-
-def create_model_files_column():
-    """Create the column for model file selection."""
-    with gr.Column():
-        with gr.Group(elem_classes=["section-group"]):
-            gr.Markdown("### Model Files", elem_classes=["section-title"])
-            ref_dict = gr.Dropdown(
-                choices=["Select a file"] + list_reference_files(),
-                label="Reference Dict JSON",
-                value="data/reference_data/reference_image_data.json"
-            )
-            index_map = gr.Dropdown(
-                choices=["Select a file"] + list_mapping_files(),
-                label="Index to Class Mapping JSON",
-                value="ckpts/index_to_class_mapping.json"
-            )
-            classifier_model = gr.Dropdown(
-                choices=["Select a file"] + list_classifier_files(),
-                label="Classifier Model (.pth)",
-                value="ckpts/SlimFace_efficientnet_b3_full_model.pth"
-            )
-            edgeface_model = gr.Dropdown(
-                choices=["Select a file"] + list_edgeface_files(),
-                label="EdgeFace Model (.pt)",
-                value="ckpts/idiap/edgeface_s_gamma_05.pt"
-            )
-    return ref_dict, index_map, classifier_model, edgeface_model
-
-def create_settings_column():
-    """Create the column for advanced settings."""
-    with gr.Column():
-        with gr.Group(elem_classes=["section-group"]):
-            gr.Markdown("### Advanced Settings", elem_classes=["section-title"])
-            algorithm = gr.Dropdown(
-                choices=["yolo", "mtcnn", "retinaface"],
-                label="Detection Algorithm",
-                value="yolo"
-            )
-            accelerator = gr.Dropdown(
-                choices=["auto", "cpu", "cuda", "mps"],
-                label="Accelerator",
-                value="auto"
-            )
-            resolution = gr.Slider(
-                minimum=128,
-                maximum=512,
-                step=32,
-                label="Image Resolution",
-                value=300
-            )
-            similarity_threshold = gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                step=0.05,
-                label="Similarity Threshold",
-                value=0.3
-            )
-    return algorithm, accelerator, resolution, similarity_threshold
-
-def create_interface():
-    """Create the Gradio interface for SlimFace."""
-    with gr.Blocks(css="gradio_app/static/styles.css", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("# SlimFace Demonstration")
-        gr.Markdown(CONTENT_DESCRIPTION)
-        gr.HTML(CONTENT_IN)
-        
-        with gr.Row():
-            image_input, output = create_image_input_column()
-            ref_dict, index_map, classifier_model, edgeface_model = create_model_files_column()
-        
-        with gr.Row():
-            algorithm, accelerator, resolution, similarity_threshold = create_settings_column()
-        
-        with gr.Row():
-            submit_btn = gr.Button("Run Inference", variant="primary", elem_classes=["centered-button"])
-        
-        submit_btn.click(
-            fn=run_inference,
-            inputs=[
-                image_input,
-                ref_dict,
-                index_map,
-                classifier_model,
-                edgeface_model,
-                algorithm,
-                accelerator,
-                resolution,
-                similarity_threshold
-            ],
-            outputs=output
-        )
-        gr.Markdown(CONTENT_OUT)
-    return demo
-
-def main():
-    """Launch the Gradio interface."""
-    demo = create_interface()
-    demo.launch()
-
-if __name__ == "__main__":
-    main()

SlimFace/apps/gradio_app/components.py

@@ -1,135 +0,0 @@
-import os
-
-# File listing functions
-def list_reference_files():
-    ref_dir = "data/reference_data/"
-    try:
-        files = [os.path.join(ref_dir, f) for f in os.listdir(ref_dir) if f.endswith(".json")]
-        return files if files else ["No .json files found in data/reference_data/"]
-    except FileNotFoundError:
-        return ["Directory data/reference_data/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_mapping_files():
-    map_dir = "ckpts/"
-    try:
-        files = [os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".json")]
-        return files if files else ["No .json files found in ckpts/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_classifier_files():
-    clf_dir = "ckpts/"
-    try:
-        files = [os.path.join(clf_dir, f) for f in os.listdir(clf_dir) if f.endswith(".pth")]
-        return files if files else ["No .pth files found in ckpts/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-def list_edgeface_files():
-    ef_dir = "ckpts/idiap/"
-    try:
-        files = [os.path.join(ef_dir, f) for f in os.listdir(ef_dir) if f.endswith(".pt")]
-        return files if files else ["No .pt files found in ckpts/idiap/"]
-    except FileNotFoundError:
-        return ["Directory ckpts/idiap/ not found"]
-    except Exception as e:
-        return [f"Error listing files: {str(e)}"]
-
-CONTENT_DESCRIPTION = """
-**SlimFace: Advanced Face Classification with TorchVision Backbones**
-"""
-CONTENT_IN = """
-<style>
-    body {
-        font-family: Arial, sans-serif;
-        line-height: 1.6;
-        margin: 0; /* Remove default margin for full-width */
-        padding: 20px; /* Adjust padding for content spacing */
-        color: #333;
-        width: 100%; /* Ensure body takes full width */
-        box-sizing: border-box; /* Include padding in width calculation */
-    }
-    .attribution {
-        background-color: #f9f9f9;
-        padding: 20px;
-        border-radius: 8px;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-    }
-    .quote-container {
-        border-left: 5px solid #007bff;
-        padding-left: 15px;
-        margin-bottom: 15px;
-        font-style: italic;
-    }
-    .attribution p {
-        margin: 10px 0;
-    }
-    .badge {
-        display: inline-block;
-        border-radius: 4px;
-        text-decoration: none;
-        font-size: 14px;
-        transition: background-color 0.3s;
-    }
-    .badge:hover {
-        background-color: #0056b3;
-    }
-    .badge img {
-        vertical-align: middle;
-        margin-right: 5px;
-    }
-    .source {
-        color: #555;
-    }
-</style>
-    <div class="quote-container">
-        <p>
-            This project leverages code from 
-            <a class="badge" href="https://github.com/otroshi/edgeface">
-                <img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
-            </a>
-            by 
-            <a class="badge" href="https://github.com/otroshi">
-                <img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
-            </a>, 
-            with our own bug fixes and enhancements available at 
-            <a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
-                <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
-            </a>.
-        </p>
-    </div>
-    <p class="source">
-        For more information, you can follow below:<br>
-        Source code: 
-        <a class="badge" href="https://github.com/danhtran2mind/SlimFace">
-            <img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
-            ,
-        </a>
-        Author: 
-        <a class="badge" href="https://github.com/danhtran2mind">
-            <img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
-            ,
-        </a>
-        PyTorch Docs: 
-        <a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
-            <img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
-        </a>
-    </p>
-"""
-
-CONTENT_OUT = """
-## More Information about SlimFace
-
-SlimFace empowers developers to build high-accuracy face classification models using transfer learning, leveraging TorchVision's powerful pre-trained architectures. 🌟 It provides a flexible, efficient, and scalable solution for facial recognition, delivering top-tier performance for custom applications.
-
-**Supported Architectures:**  
-- **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸  
-- **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻  
-- **Vision Transformers (ViT)**: B_16, B_32, H_14, L_16, L_32 for cutting-edge feature extraction. 🚀  
-"""

SlimFace/apps/gradio_app/inference.py

@@ -1,93 +0,0 @@
-import os
-import sys
-from PIL import Image
-
-# Append the path to the inference script's directory
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'slimface', 'inference')))
-
-from end2end_inference import inference_and_confirm
-
-def run_inference(image, reference_dict_path, index_to_class_mapping_path, model_path,
-                 edgeface_model_path="ckpts/idiap/edgeface_base.pt", 
-                 algorithm="yolo", accelerator="auto", resolution=224, similarity_threshold=0.6):
-    
-    # Validate image input
-    if image is None:
-        return '<div class="error-message">Error: No image provided. Please upload an image.</div>'
-
-    # Define temporary image path
-    temp_image_path = os.path.join(os.path.dirname(__file__), "temp_data", "temp_image.jpg")
-    os.makedirs(os.path.dirname(temp_image_path), exist_ok=True)
-    
-    # Save the image
-    try:
-        image.save(temp_image_path)
-    except Exception as e:
-        return f'<div class="error-message">Error saving image: {str(e)}</div>'
-
-    # Create args object to mimic command-line arguments
-    class Args:
-        def __init__(self):
-            self.unknown_image_path = temp_image_path
-            self.reference_dict_path = reference_dict_path.name if hasattr(reference_dict_path, 'name') else reference_dict_path
-            self.index_to_class_mapping_path = index_to_class_mapping_path.name if hasattr(index_to_class_mapping_path, 'name') else index_to_class_mapping_path
-            self.model_path = model_path.name if hasattr(model_path, 'name') else model_path
-            self.edgeface_model_path = edgeface_model_path.name if hasattr(edgeface_model_path, 'name') else edgeface_model_path
-            self.algorithm = algorithm
-            self.accelerator = accelerator
-            self.resolution = resolution
-            self.similarity_threshold = similarity_threshold
-
-    args = Args()
-
-    # Validate inputs
-    if not all([args.reference_dict_path, args.index_to_class_mapping_path, args.model_path]):
-        return '<div class="error-message">Error: Please provide all required files (reference dict, index-to-class mapping, and model).</div>'
-
-    try:
-        # Call the inference function from end2end_inference.py
-        results = inference_and_confirm(args)
-        
-        # Format output as HTML for Gradio
-        output = '<div class="results-container">'
-        output += '<h2 class="result-title">Inference Results</h2>'
-        
-        if not results:
-            output += '<div class="error-message">No results returned from inference.</div>'
-        else:
-            for idx, result in enumerate(results, 1):
-                output += '<div class="result-card">'
-                output += f'<h3 class="result-title">Result {idx}</h3>'
-                
-                # Person Name
-                person_name = result.get('predicted_class', 'N/A')
-                output += f'<div class="result-item"><span class="label">Person Name</span><span class="value">{person_name}</span></div>'
-                
-                # Confidence
-                confidence = result.get('confidence', 'N/A')
-                confidence_str = f'{confidence:.4f}' if isinstance(confidence, (int, float)) else 'N/A'
-                output += f'<div class="result-item"><span class="label">Confidence</span><span class="value">{confidence_str}</span></div>'
-                
-                # Similarity with Reference Image
-                similarity = result.get('similarity', 'N/A')
-                similarity_str = f'{similarity:.4f}' if isinstance(similarity, (int, float)) else 'N/A'
-                output += f'<div class="result-item"><span class="label">Similarity with<br>Reference Image</span><span class="value">{similarity_str}</span></div>'
-                
-                # Confirmed Person
-                confirmed = result.get('confirmed', 'N/A')
-                confirmed_class = 'confirmed-true' if confirmed is True else 'confirmed-false' if confirmed is False else ''
-                confirmed_str = str(confirmed) if confirmed is not None else 'N/A'
-                output += f'<div class="result-item"><span class="label">Confirmed Person</span><span class="value {confirmed_class}">{confirmed_str}</span></div>'
-                
-                output += '</div>'
-        
-        output += '</div>'
-        return output
-    
-    except Exception as e:
-        return f'<div class="error-message">Error during inference: {str(e)}</div>'
-    
-    finally:
-        # Clean up temporary image
-        if os.path.exists(temp_image_path):
-            os.remove(temp_image_path)

SlimFace/apps/gradio_app/static/script.js

@@ -1,3 +0,0 @@
-// Placeholder for future JavaScript functionality
-// Currently, no JavaScript is required for the Gradio app as interactions are handled by Gradio
-console.log("SlimFace Gradio App JavaScript loaded");

SlimFace/apps/gradio_app/static/style.css

@@ -1,229 +0,0 @@
-body {
-    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
-    background: linear-gradient(145deg, #e2e8f0 0%, #b8c6db 100%);
-    margin: 0;
-    padding: 0;
-    min-height: 100vh;
-    color: #1a202c;
-}
-
-.gradio-container {
-    max-width: 1280px;
-    margin: 0 auto;
-    padding: 2.5rem 1.5rem;
-    box-sizing: border-box;
-}
-
-h1 {
-    color: #1a202c;
-    font-size: 2.75rem;
-    font-weight: 800;
-    text-align: center;
-    margin-bottom: 2.5rem;
-    letter-spacing: -0.025em;
-    background: linear-gradient(to right, #2b6cb0, #4a90e2);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-}
-
-.section-title {
-    color: #1a202c;
-    font-size: 1.5rem;
-    font-weight: 700;
-    margin-bottom: 1rem;
-    border-bottom: 2px solid #4a90e2;
-    padding-bottom: 0.5rem;
-    letter-spacing: -0.015em;
-}
-
-.section-group {
-    background: rgba(255, 255, 255, 0.95);
-    border-radius: 0.5rem;
-    padding: 1.5rem;
-    border: 1px solid rgba(226, 232, 240, 0.5);
-    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-}
-
-.results-container {
-    display: flex;
-    flex-direction: column;
-    gap: 1.75rem;
-    padding: 2rem;
-    background: rgba(255, 255, 255, 0.95);
-    border-radius: 1.25rem;
-    box-shadow: 0 10px 20px rgba(0, 0, 0, 0.15), 0 4px 6px rgba(0, 0, 0, 0.1);
-    border: 1px solid rgba(226, 232, 240, 0.5);
-    backdrop-filter: blur(8px);
-}
-
-.result-card {
-    background: linear-gradient(145deg, #f7fafc, #edf2f7);
-    border-radius: 1rem;
-    padding: 2.25rem;
-    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.1);
-    transition: transform 0.3s ease, box-shadow 0.3s ease, background 0.3s ease;
-    position: relative;
-    overflow: hidden;
-}
-
-.result-card:hover {
-    transform: translateY(-5px);
-    box-shadow: 0 10px 24px rgba(0, 0, 0, 0.15);
-    background: linear-gradient(145deg, #ffffff, #e6eefa);
-}
-
-.result-card::before {
-    content: '';
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 4px;
-    background: linear-gradient(to right, #4a90e2, #63b3ed);
-    transition: height 0.3s ease;
-}
-
-.result-card:hover::before {
-    height: 8px;
-}
-
-.result-title {
-    color: #1a202c;
-    font-size: 1.875rem;
-    font-weight: 700;
-    margin-bottom: 1.5rem;
-    border-bottom: 3px solid #4a90e2;
-    padding-bottom: 0.75rem;
-    letter-spacing: -0.015em;
-}
-
-.result-item {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    margin: 1rem 0;
-    font-size: 1.125rem;
-    color: #2d3748;
-    line-height: 1.6;
-}
-
-.label {
-    font-weight: 600;
-    color: #2b6cb0;
-    text-align: left;
-    text-transform: uppercase;
-    font-size: 0.95rem;
-    letter-spacing: 0.05em;
-    flex: 0 0 auto;
-}
-
-.value {
-    color: #1a202c;
-    font-weight: 500;
-    text-align: right;
-    flex: 0 0 auto;
-}
-
-.value.confirmed-true {
-    color: #2f855a;
-    font-weight: 600;
-    background: #c6f6d5;
-    padding: 0.25rem 0.5rem;
-    border-radius: 0.375rem;
-}
-
-.value.confirmed-false {
-    color: #c53030;
-    font-weight: 600;
-    background: #fed7d7;
-    padding: 0.25rem 0.5rem;
-    border-radius: 0.375rem;
-}
-
-.error-message {
-    background: #fef2f2;
-    color: #9b2c2c;
-    padding: 1.75rem;
-    border-radius: 0.875rem;
-    margin: 1.25rem 0;
-    font-size: 1.125rem;
-    font-weight: 500;
-    border: 1px solid #e53e3e;
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-}
-
-.centered-button {
-    display: block;
-    margin: 1rem auto;
-    background: #4a90e2;
-    color: white;
-    padding: 0.75rem 1.5rem;
-    border-radius: 0.5rem;
-    border: none;
-    font-size: 1rem;
-    font-weight: 600;
-    cursor: pointer;
-    transition: background 0.3s ease;
-    position: relative;
-    padding-left: 2.5rem;
-    width: 30%;
-}
-
-.centered-button:hover {
-    background: #2b6cb0;
-}
-
-.centered-button::after {
-    content: '🤔';
-    position: absolute;
-    left: 0.75rem;
-    top: 50%;
-    transform: translateY(-50%);
-    font-size: 1.2rem;
-}
-
-@media (max-width: 768px) {
-    .gradio-container {
-        padding: 1.5rem;
-    }
-    
-    h1 {
-        font-size: 2rem;
-    }
-    
-    .results-container {
-        padding: 1.5rem;
-    }
-    
-    .result-card {
-        padding: 1.5rem;
-    }
-    
-    .result-title {
-        font-size: 1.5rem;
-    }
-    
-    .result-item {
-        font-size: 1rem;
-        flex-direction: column;
-        align-items: flex-start;
-        gap: 0.5rem;
-    }
-    
-    .label, .value {
-        text-align: left;
-    }
-    
-    .section-title {
-        font-size: 1.25rem;
-    }
-    
-    .section-group {
-        padding: 1rem;
-    }
-    
-    .centered-button {
-        padding: 0.5rem 1rem;
-        font-size: 0.9rem;
-    }
-}

SlimFace/assets/comparision.md

@@ -1,11 +0,0 @@
-slim_face_vit_b_16
-    Train loss: 0.0074, Train acc: 0.9980, Val loss: 0.2179, Val acc: 0.9336
-    
-efficientnet_b3
-    Train loss: 0.0014, Train acc: 1.0000, Val loss: 0.1931, Val acc: 0.9427
-
-efficientnet_v2_s
-    Train loss: 0.0016, Train acc: 1.0000, Val loss: 0.2374, Val acc: 0.9375
-
-regnet_y_800mf
-    Train loss: 0.0033, Train acc: 0.9997, Val loss: 0.3766, Val acc: 0.8906

SlimFace/ckpts/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this .gitignore file
-!.gitignore

SlimFace/configs/accelerate_config.yaml

@@ -1,6 +0,0 @@
-compute_environment: LOCAL_MACHINE
-distributed_type: FSDP
-num_processes: 4
-mixed_precision: fp16
-fsdp_config:
-  fsdp_offload_params: true

SlimFace/configs/image_classification_models_config.yaml

@@ -1,249 +0,0 @@
-# For more details on models, see https://pytorch.org/vision/main/models.html
-
-# EfficientNet models: Designed for efficiency with compound scaling of depth, width, and resolution.
-# These models balance accuracy and computational efficiency, ideal for resource-constrained environments.
-efficientnet_b0:
-  metrics:
-    Acc@1: 77.692  # Top-1 accuracy on ImageNet
-    Acc@5: 93.532  # Top-5 accuracy on ImageNet
-    GFLOPS: 0.39   # Computational complexity
-    Params: 5.3M   # Number of parameters
-  model_fn: models.efficientnet_b0
-  resolution: 224  # Input image resolution
-  weights: models.EfficientNet_B0_Weights.IMAGENET1K_V1  # Pretrained weights on ImageNet
-
-efficientnet_b1:
-  metrics:
-    Acc@1: 78.642
-    Acc@5: 94.186
-    GFLOPS: 0.69
-    Params: 7.8M
-  model_fn: models.efficientnet_b1
-  resolution: 240
-  weights: models.EfficientNet_B1_Weights.IMAGENET1K_V1
-
-efficientnet_b2:
-  metrics:
-    Acc@1: 80.608
-    Acc@5: 95.31
-    GFLOPS: 1.09
-    Params: 9.1M
-  model_fn: models.efficientnet_b2
-  resolution: 260
-  weights: models.EfficientNet_B2_Weights.IMAGENET1K_V1
-
-efficientnet_b3:
-  metrics:
-    Acc@1: 82.008
-    Acc@5: 96.054
-    GFLOPS: 1.83
-    Params: 12.2M
-  model_fn: models.efficientnet_b3
-  resolution: 300
-  weights: models.EfficientNet_B3_Weights.IMAGENET1K_V1
-
-efficientnet_b4:
-  metrics:
-    Acc@1: 83.384
-    Acc@5: 96.594
-    GFLOPS: 4.39
-    Params: 19.3M
-  model_fn: models.efficientnet_b4
-  resolution: 380
-  weights: models.EfficientNet_B4_Weights.IMAGENET1K_V1
-
-efficientnet_b5:
-  metrics:
-    Acc@1: 83.444
-    Acc@5: 96.628
-    GFLOPS: 10.27
-    Params: 30.4M
-  model_fn: models.efficientnet_b5
-  resolution: 456
-  weights: models.EfficientNet_B5_Weights.IMAGENET1K_V1
-
-efficientnet_b6:
-  metrics:
-    Acc@1: 84.008
-    Acc@5: 96.916
-    GFLOPS: 19.07
-    Params: 43.0M
-  model_fn: models.efficientnet_b6
-  resolution: 528
-  weights: models.EfficientNet_B6_Weights.IMAGENET1K_V1
-
-efficientnet_b7:
-  metrics:
-    Acc@1: 84.122
-    Acc@5: 96.908
-    GFLOPS: 37.75
-    Params: 66.3M
-  model_fn: models.efficientnet_b7
-  resolution: 600
-  weights: models.EfficientNet_B7_Weights.IMAGENET1K_V1
-
-# EfficientNet V2 models: Improved training efficiency and performance over V1.
-# These models use progressive learning and optimized scaling for better accuracy.
-efficientnet_v2_l:
-  metrics:
-    Acc@1: 85.808
-    Acc@5: 97.788
-    GFLOPS: 56.08
-    Params: 118.5M
-  model_fn: models.efficientnet_v2_l
-  resolution: 480
-  weights: models.EfficientNet_V2_L_Weights.IMAGENET1K_V1
-
-efficientnet_v2_m:
-  metrics:
-    Acc@1: 85.112
-    Acc@5: 97.156
-    GFLOPS: 24.58
-    Params: 54.1M
-  model_fn: models.efficientnet_v2_m
-  resolution: 480
-  weights: models.EfficientNet_V2_M_Weights.IMAGENET1K_V1
-
-efficientnet_v2_s:
-  metrics:
-    Acc@1: 84.228
-    Acc@5: 96.878
-    GFLOPS: 8.37
-    Params: 21.5M
-  model_fn: models.efficientnet_v2_s
-  resolution: 384
-  weights: models.EfficientNet_V2_S_Weights.IMAGENET1K_V1
-
-# RegNet models: Designed for scalability and efficiency with a focus on network design.
-# These models optimize for both accuracy and computational efficiency.
-regnet_y_128gf:
-  metrics:
-    Acc@1: 86.068  # High accuracy but computationally expensive
-    Acc@5: 97.844
-    GFLOPS: 127.52
-    Params: 644.8M
-  model_fn: models.regnet_y_128gf
-  resolution: 224
-  weights: models.RegNet_Y_128GF_Weights.IMAGENET1K_SWAG_LINEAR_V1
-
-regnet_y_16gf:
-  metrics:
-    Acc@1: 82.886
-    Acc@5: 96.328
-    GFLOPS: 15.91
-    Params: 83.6M
-  model_fn: models.regnet_y_16gf
-  resolution: 224
-  weights: models.RegNet_Y_16GF_Weights.IMAGENET1K_V2
-
-regnet_y_1_6gf:
-  metrics:
-    Acc@1: 80.876
-    Acc@5: 95.444
-    GFLOPS: 1.61
-    Params: 11.2M
-  model_fn: models.regnet_y_1_6gf
-  resolution: 224
-  weights: models.RegNet_Y_1_6GF_Weights.IMAGENET1K_V2
-
-regnet_y_32gf:
-  metrics:
-    Acc@1: 83.368
-    Acc@5: 96.498
-    GFLOPS: 32.28
-    Params: 145.0M
-  model_fn: models.regnet_y_32gf
-  resolution: 224
-  weights: models.RegNet_Y_32GF_Weights.IMAGENET1K_V2
-
-regnet_y_3_2gf:
-  metrics:
-    Acc@1: 81.982
-    Acc@5: 95.972
-    GFLOPS: 3.18
-    Params: 19.4M
-  model_fn: models.regnet_y_3_2gf
-  resolution: 224
-  weights: models.RegNet_Y_3_2GF_Weights.IMAGENET1K_V2
-
-regnet_y_400mf:
-  metrics:
-    Acc@1: 75.804
-    Acc@5: 92.742
-    GFLOPS: 0.4
-    Params: 4.3M
-  model_fn: models.regnet_y_400mf
-  resolution: 224
-  weights: models.RegNet_Y_400MF_Weights.IMAGENET1K_V2
-
-regnet_y_800mf:
-  metrics:
-    Acc@1: 78.828
-    Acc@5: 94.502
-    GFLOPS: 0.83
-    Params: 6.4M
-  model_fn: models.regnet_y_800mf
-  resolution: 224
-  weights: models.RegNet_Y_800MF_Weights.IMAGENET1K_V2
-
-regnet_y_8gf:
-  metrics:
-    Acc@1: 82.828
-    Acc@5: 96.33
-    GFLOPS: 8.47
-    Params: 39.4M
-  model_fn: models.regnet_y_8gf
-  resolution: 224
-  weights: models.RegNet_Y_8GF_Weights.IMAGENET1K_V2
-
-# Vision Transformer (ViT) models: Transformer-based architecture for image classification.
-# These models excel in capturing long-range dependencies but require significant compute for larger variants.
-vit_b_16:
-  metrics:
-    Acc@1: 81.072  # Base ViT model with balanced accuracy and efficiency
-    Acc@5: 95.318
-    GFLOPS: 17.56
-    Params: 86.6M
-  model_fn: models.vit_b_16
-  resolution: 224
-  weights: models.ViT_B_16_Weights.IMAGENET1K_V1
-
-vit_b_32:
-  metrics:
-    Acc@1: 75.912  # Smaller patch size version of ViT, lower accuracy but fewer computations
-    Acc@5: 92.466
-    GFLOPS: 4.41
-    Params: 88.2M
-  model_fn: models.vit_b_32
-  resolution: 224
-  weights: models.ViT_B_32_Weights.IMAGENET1K_V1
-
-vit_h_14:
-  metrics:
-    Acc@1: 88.552  # High-performance ViT model with very high accuracy and computational cost
-    Acc@5: 98.694
-    GFLOPS: 1016.72
-    Params: 633.5M
-  model_fn: models.vit_h_14
-  resolution: 224
-  weights: models.ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1
-
-vit_l_16:
-  metrics:
-    Acc@1: 79.662  # Larger ViT model with improved accuracy over base models
-    Acc@5: 94.638
-    GFLOPS: 61.55
-    Params: 304.3M
-  model_fn: models.vit_l_16
-  resolution: 224
-  weights: models.ViT_L_16_Weights.IMAGENET1K_V1
-
-vit_l_32:
-  metrics:
-    Acc@1: 76.972  # Larger ViT with larger patch size, trading accuracy for reduced compute
-    Acc@5: 93.07
-    GFLOPS: 15.38
-    Params: 306.5M
-  model_fn: models.vit_l_32
-  resolution: 224
-  weights: models.ViT_L_32_Weights.IMAGENET1K_V1

SlimFace/data/raw/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this .gitignore file
-!.gitignore