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09_pretrained_effnetb3_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7bedd1070c97d5d240622557659a5ebb6d46638cd2aafa553edeab6e661803f0
+size 43397490

app.py

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+import gradio as gr
+import torch 
+import torchvision
+import os 
+
+from model import model_efficientb3
+from timeit import default_timer as Timer
+from typing import Tuple,Dict
+
+class_name=["pizza","steak","sushi"]
+
+effnetb3,effentb3_tranform=model_efficientb3(out_feature=3)
+
+effnetb3.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb3_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu")
+    )
+)
+
+def predict(img) -> Tuple[Dict,float]:
+
+    start_time=Timer()
+
+    img=effnetb3_tranform(img).unsqueeze(0)
+
+    effnetb3.eval()
+    with torch.inference_mode():
+        pred_probs=torch.softmax(effnetb3(img),dim=1)
+
+    pred_labels_and_probs={class_name[i]: float(pred_probs[0][i]) for i in range(len(class_name))}
+
+    pred_time=round(Timer()-start_time,5)
+
+    return pred_labels_and_probs,pred_time
+
+
+title="FoodVision Mini 🍕🥩🍣"
+description= "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article="tryin to learn pytorch"
+
+demo=gr.Interface(fn=predict,
+                  inputs=gr.Image(type="pil"),
+                  outputs=[gr.Label(num_top_classes=3,label="Prediction"),
+                           gr.Number(label="Prediction time (s)")],
+                  examples=example_list,
+                  title=title,
+                  description=description,
+                  article=article)
+
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def model_efficientb3(out_feature:int=3,
+                      p:int=0.3):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+
+    weights=torchvision.models.EfficientNet_B3_Weights.DEFAULT
+    transform=weights.transforms()
+    model=torchvision.models.efficientnet_b3(weights=weights)
+
+    for params in model.parameters():
+        params.requires_grad=False
+
+    print(model.classifier)
+
+    model.classifier=nn.Sequential(
+        nn.Dropout(p=p,inplace=True),
+        nn.Linear(in_features=1536,out_features=out_feature,bias=True)
+    )
+
+    print(f"the new classifier as per your request \n {model.classifier}")
+
+    return model,transform

requirements.txt

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+torch==2.2.0
+torchvision==0.17.0
+gradio==4.20.1