FoodVision Big all files added

.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
+examples/04-pizza-dad.jpg filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdbba73fd4e8ba6431756c1f4cab806bb10e3f3ff9f0165c0b952f69ca510ff2
+size 31857210

app.py

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+
+### 1. Imports and class names setup ###
+import gradio as gr
+import torch
+import os
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+
+# Setup class names
+with open('/content/demos/foodvision_big/class_names.txt', 'r') as f:
+  class_names = [food_name.strip() for food_name in f.readlines()]
+
+### 2. Model and transforms preparation ###
+# Create model and transforms
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=len(class_names),
+)
+
+# Load save weights
+effnetb2.load_state_dict(torch.load('demos/foodvision_big/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth', map_location=torch.device("cpu"))) # load the model to the CPU
+
+### 3. Predict function ###
+def predict(img) -> tuple[dict, float]:
+    # Start a timer
+    start_time = timer()
+    
+    # Transform the input image for use with EffNetB2
+    img = effnetb2_transforms(img).unsqueeze(dim=0) # unsqueeze = add batch dimension on 0th index
+    
+    # Put model into eval mode, make prediction
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass transformed image through the model and turn the prediction logits into probabilities
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {
+        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
+    }
+    
+    # Calculate pred time
+    end_time = timer()
+    pred_time = round(end_time - start_time, 4)
+    
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article
+title = "FoodVision BIG 🍔👁💪"
+descripton = "An EfficientNetB2 Feature Extractor computer vision model to classify 101 classes of food from the Food101 dataset."
+article = "Created at 09. PyTorch Model Deployment."
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # maps inputs to outputs
+                    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=descripton,
+                    article=article)
+
+# Launch Demo!
+demo.launch()

class_names.txt

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+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

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+
+import torch
+import torchvision
+from torch import nn
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)
+                          seed:int=42):
+    # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.efficientnet.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet.efficientnet_b2(weights=weights).to(device)
+    
+    # 4. Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+    
+    # 5. Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes, bias=True)
+    ).to(device)
+    
+    return model, transforms

requirements.txt

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+
+torch
+torchvision
+gradio