init comit

EffB2_food_big.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a815dc64e9e98586947c7b136bb27437abcb7e6064733eb4a805c9e7166ea2c
+size 31829435

app.py

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+import os
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+import gradio as gr
+import torch
+
+from model import create_model
+
+title = "Food Vision Mini by Edesak"
+desc = "EffitientNetB2 for recognition of Food with 101 classes from [Zero To Mastery Course](https://www.udemy.com/course/pytorch-for-deep-learning/). Used Dataset if Food 101"
+article = "My Github page [Edesak](https://github.com/Edesak)"
+
+filename = "labels.txt"
+
+with open(filename, "r") as file:
+    class_names = file.read().split("\n")
+class_names = class_names[:-1]
+
+model, transform = create_model()
+model.load_state_dict(torch.load(f="EffB2_food_big.pth", map_location=torch.device('cpu')))
+model.to("cpu")
+example_list = [["Examples/" + example] for example in os.listdir("Examples")]
+
+
+def predict(img) -> Tuple[Dict, float]:
+    start_timer = timer()
+    img = transform(img).unsqueeze(0)
+
+    model.eval()
+    with torch.inference_mode():
+        y = torch.softmax(model(img), dim=1)
+
+    pred_labels = {class_names[i]: float(y[0][i]) for i in range(len(class_names))}
+    end_time = timer()
+    pred_time = round(end_time - start_timer, 4)
+    return pred_labels, pred_time
+
+
+demo = gr.Interface(fn=predict, inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), gr.Number(label="Prediction time (s)")],
+                    title=title,
+                    description=desc,
+                    article=article,
+                    examples=example_list)
+
+demo.launch()

labels.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
+Cheesecake
+Cheese plate
+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
+from torch.nn import Dropout, Linear
+from torchvision.models import EfficientNet_B2_Weights, efficientnet_b2
+
+
+def create_model():
+    weights = EfficientNet_B2_Weights.DEFAULT
+    model = efficientnet_b2(weights=weights)
+    transform = weights.transforms()
+
+    classifier = torch.nn.Sequential(
+        Dropout(p=0.3, inplace=True),
+        Linear(in_features=1408, out_features=101)
+    )
+    for layer in model.features:
+        layer.requires_grad_(False)
+    model.classifier = classifier
+    return model, transform

requirements.txt

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+gradio==3.33.1
+torch==1.13.0
+torchvision==0.14.0