Initial deploy

README.md

@@ -1,12 +1,17 @@
 ---
 title: Image Classification
-emoji: 🐨
-colorFrom: yellow
-colorTo: indigo
+emoji: "\U0001F3AF"
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 6.11.0
+sdk_version: "5.29.0"
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Image Classification — ResNet / ViT / MobileNet
+
+Upload an image and compare predictions across different CNN and Transformer architectures.
+
+**Course**: 100 Deep Learning ch2 — Convolutional Neural Networks

app.py

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+"""
+Image Classification — Compare ResNet-50 / ViT-base / MobileNetV3
+Course: 100 Deep Learning ch2
+"""
+
+import json
+import urllib.request
+
+import torch
+import torch.nn.functional as F
+import torchvision.models as models
+import torchvision.transforms as T
+import timm
+import gradio as gr
+from PIL import Image
+
+device = torch.device("cpu")
+
+# ---------------------------------------------------------------------------
+# Models
+# ---------------------------------------------------------------------------
+model_registry = {
+    "ResNet-50": models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1),
+    "MobileNetV3-Small": models.mobilenet_v3_small(weights=models.MobileNet_V3_Small_Weights.IMAGENET1K_V1),
+    "ViT-Base (timm)": timm.create_model("vit_base_patch16_224", pretrained=True),
+}
+for m in model_registry.values():
+    m.eval().to(device)
+
+# ---------------------------------------------------------------------------
+# Preprocessing
+# ---------------------------------------------------------------------------
+preprocess = T.Compose([
+    T.Resize(256),
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# ImageNet labels
+LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
+try:
+    with urllib.request.urlopen(LABELS_URL) as resp:
+        LABELS = json.loads(resp.read().decode())
+except Exception:
+    LABELS = [str(i) for i in range(1000)]
+
+
+# ---------------------------------------------------------------------------
+# Classify
+# ---------------------------------------------------------------------------
+def classify(image: Image.Image, model_name: str):
+    if image is None:
+        return {}
+    img = image.convert("RGB")
+    tensor = preprocess(img).unsqueeze(0).to(device)
+
+    model = model_registry[model_name]
+    with torch.no_grad():
+        logits = model(tensor)
+
+    probs = F.softmax(logits, dim=1)[0]
+    top5 = torch.topk(probs, 5)
+    return {LABELS[idx]: float(prob) for prob, idx in zip(top5.values, top5.indices)}
+
+
+def compare_all(image: Image.Image):
+    """Run all 3 models and return results."""
+    if image is None:
+        return {}, {}, {}
+    r1 = classify(image, "ResNet-50")
+    r2 = classify(image, "MobileNetV3-Small")
+    r3 = classify(image, "ViT-Base (timm)")
+    return r1, r2, r3
+
+
+# ---------------------------------------------------------------------------
+# UI
+# ---------------------------------------------------------------------------
+with gr.Blocks(title="Image Classification") as demo:
+    gr.Markdown(
+        "# Image Classification\n"
+        "Upload an image to compare predictions from different architectures.\n"
+        "*Course: 100 Deep Learning ch2 — CNN*"
+    )
+
+    with gr.Tab("Single Model"):
+        with gr.Row():
+            with gr.Column():
+                img_single = gr.Image(type="pil", label="Upload Image")
+                model_choice = gr.Dropdown(
+                    list(model_registry.keys()), value="ResNet-50", label="Model"
+                )
+                btn_single = gr.Button("Classify", variant="primary")
+            with gr.Column():
+                out_single = gr.Label(num_top_classes=5, label="Top-5 Predictions")
+
+        btn_single.click(classify, [img_single, model_choice], out_single)
+
+    with gr.Tab("Compare All Models"):
+        with gr.Row():
+            img_compare = gr.Image(type="pil", label="Upload Image")
+            btn_compare = gr.Button("Compare All", variant="primary")
+        with gr.Row():
+            out_resnet = gr.Label(num_top_classes=5, label="ResNet-50")
+            out_mobile = gr.Label(num_top_classes=5, label="MobileNetV3-Small")
+            out_vit = gr.Label(num_top_classes=5, label="ViT-Base")
+
+        btn_compare.click(compare_all, [img_compare], [out_resnet, out_mobile, out_vit])
+
+    gr.Examples(
+        examples=[
+            ["examples/cat.jpg"],
+            ["examples/dog.jpg"],
+            ["examples/car.jpg"],
+        ],
+        inputs=[img_single],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=5.0.0
+torch>=2.0.0
+torchvision>=0.15.0
+timm>=0.9.0
+Pillow