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09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ae2e70bf6105ef2b8789699268c7ca120fe480eba2b66ddf770f5e0125341fd
+size 31314554

app.py

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+
+import gradio as gr
+import os
+import torch 
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_names = ["pizza", "steak", "sushi"]
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+
+effnetb2.load_state_dict(torch.load("09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", map_location=torch.device("cpu")))
+
+def predict(img) -> Tuple[Dict, float]:
+    start_time = timer()
+
+    img = effnetb2_transforms(img).unsqueeze(0)
+
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in  range(len(class_names))}
+
+    end_timer = timer()
+    pred_time = round(end_timer-start_time, 4)
+
+    return pred_labels_and_probs, pred_time
+
+example_list =[["examples/" + example] for example in os.listdir("examples")]
+
+import gradio as gr
+
+title="FoodVision Mini 🍕🥩🍣"
+description = "An EfficientNetB2 feature extractor model that predicts pizza, steak and sushi"
+article= "Created as a test"
+
+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)")], 
+                    examples=example_list, 
+                    title=title, 
+                    description=description,
+                    article=article)
+
+demo.launch(debug=False, share=True)
+

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3,
+                          seed:int=42):
+    """Creates an EfficientNet-B2 feature extractor model and transforms."""
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights).to(device)
+
+    for param in model.parameters():
+        param.requires_grad = False
+
+    
+    set_seeds()
+
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes, bias=True)
+    )
+
+    return model, transforms

requirements.txt

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+
+torch==2.2.0.dev20230922+cu121
+torchvision==0.17.0.dev20230925+cu121
+gradio==3.50.2