first 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
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:063541830c9667af5968c73d01e895e3a07de182df71a3dfe926cc1f8a638a3b
+size 31313869

app.py

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+import gradio as gr
+import os
+import torch
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+from model import create_effnetb2_model
+
+class_names = ["pizza", "steak", "sushi"]
+
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
+
+effnetb2_weigths = effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu"),
+    )
+)
+
+
+def predict(img) -> Tuple[Dict, float]:
+    start = 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()
+    pred_time = round(end - start, 4)
+
+    return pred_labels_and_probs, pred_time
+
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+
+app = 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="FoodVision Mini",
+    description="An EFficientNetB2 feature extractor for pizza, sushi. and steak",
+    article="Created at [09_model_deployment]",
+)
+
+app.launch()

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):
+    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)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes),
+    )
+
+    return model, transforms

requirements.txt

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+torch
+torchvision
+gradio