initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pretrained_effnetb2_feature_extractor_food101_20_percent.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,59 @@
+import os
+import gradio as gr
+import torch
+import torchvision
+from typing import Tuple, Dict
+from timeit import default_timer as timer
+from model import create_effnetb2_model
+
+with open("class_names.txt", "r") as f:
+  class_names = [food_name.strip() for food_name in f.readlines()]
+
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=101
+)
+
+effnetb2.load_state_dict(
+    torch.load(f="pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+               map_location=torch.device("cpu")) # Load the model to the CPU
+)
+
+def predict(img) -> Tuple[Dict, float]:
+
+  start_time = timer()
+
+  transformed_image = effnetb2_transforms(img).unsqueeze(dim=0)
+
+  effnetb2.eval()
+
+  with torch.inference_mode():
+
+    pred_probs = torch.softmax(effnetb2(transformed_image), dim=1)
+
+  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+  end_time = timer()
+
+  pred_time = round(end_time-start_time, 4)
+
+  return pred_labels_and_probs, pred_time
+
+
+title = "Food101 Classification App 🍔"
+description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model trained on [Food101 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Food101.html) which classifies 101 different food categories."
+article = "How to Use: Upload a food image in the upload section above or select an images from the 'Examples' section. " \
+          "Click on the 'Submit' button and the model will detect which" \
+          "food catagory the image belongs to."
+
+example_list = [["examples" / example] for example in os.listdir("examples")]
+
+food101_app = gr.Interface(fn=predict,
+                            inputs=gr.Image(type="pil"),
+                            outputs=[gr.Label(num_top_classes=5, label="Predictions"),
+                              gr.Number(label="Prediction Time (s)")],
+                            examples=example_list,
+                            title=title,
+                            description=description,
+                            article=article)
+
+food101_app.launch()

class_names.txt

@@ -0,0 +1,101 @@
+apple_pie
+baby_back_ribs
+baklava
+beef_carpaccio
+beef_tartare
+beet_salad
+beignets
+bibimbap
+bread_pudding
+breakfast_burrito
+bruschetta
+caesar_salad
+cannoli
+caprese_salad
+carrot_cake
+ceviche
+cheese_plate
+cheesecake
+chicken_curry
+chicken_quesadilla
+chicken_wings
+chocolate_cake
+chocolate_mousse
+churros
+clam_chowder
+club_sandwich
+crab_cakes
+creme_brulee
+croque_madame
+cup_cakes
+deviled_eggs
+donuts
+dumplings
+edamame
+eggs_benedict
+escargots
+falafel
+filet_mignon
+fish_and_chips
+foie_gras
+french_fries
+french_onion_soup
+french_toast
+fried_calamari
+fried_rice
+frozen_yogurt
+garlic_bread
+gnocchi
+greek_salad
+grilled_cheese_sandwich
+grilled_salmon
+guacamole
+gyoza
+hamburger
+hot_and_sour_soup
+hot_dog
+huevos_rancheros
+hummus
+ice_cream
+lasagna
+lobster_bisque
+lobster_roll_sandwich
+macaroni_and_cheese
+macarons
+miso_soup
+mussels
+nachos
+omelette
+onion_rings
+oysters
+pad_thai
+paella
+pancakes
+panna_cotta
+peking_duck
+pho
+pizza
+pork_chop
+poutine
+prime_rib
+pulled_pork_sandwich
+ramen
+ravioli
+red_velvet_cake
+risotto
+samosa
+sashimi
+scallops
+seaweed_salad
+shrimp_and_grits
+spaghetti_bolognese
+spaghetti_carbonara
+spring_rolls
+steak
+strawberry_shortcake
+sushi
+tacos
+takoyaki
+tiramisu
+tuna_tartare
+waffles

model.py

@@ -0,0 +1,23 @@
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3,
+                          seed:int=42):
+
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  for param in model.parameters():
+    param.requires_grad = False
+
+  torch.manual_seed(seed=42)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p=0.3, inplace=True),
+      nn.Linear(in_features=1408, out_features=num_classes)
+  )
+
+  model.name = "effnetb2"
+  print(f"Created new model: {model.name}")
+  return model, transforms

pretrained_effnetb2_feature_extractor_food101_20_percent.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11d2f79bfdeb19d7cb1dc41a2a98e120dfc84ea339e03a166c9c5ce9910886fc
+size 31855674

requirements.txt

@@ -0,0 +1,3 @@
+torch==2.3.0
+torchvision==0.18.0
+gradio==4.32.0