initial model push

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README.md

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+# Garbage Classifier · EfficientNet‑V2‑S (torchvision)
+
+Finetuned model for 10‑class garbage image classification.
+
+> **Owner:** `attilaultzindur`   •   **Pushed:** 2025-05-05

config.json

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+{
+  "architecture": "torchvision_efficientnet_v2_s",
+  "input_size": 224,
+  "num_labels": 10,
+  "label_names": [
+    "battery",
+    "biological",
+    "cardboard",
+    "clothes",
+    "glass",
+    "metal",
+    "paper",
+    "plastic",
+    "shoes",
+    "trash"
+  ],
+  "library": "torchvision"
+}

handler.py

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+from typing import Dict, Any
+import io, base64, torch, torchvision
+from PIL import Image
+from torchvision import transforms as T
+
+class EndpointHandler:
+    def __init__(self, path='.'):  # path = repo root in container
+        self.labels = ['battery', 'biological', 'cardboard', 'clothes', 'glass', 'metal', 'paper', 'plastic', 'shoes', 'trash']
+        self.model = torchvision.models.efficientnet_v2_s(weights=None)
+        nf = self.model.classifier[1].in_features
+        self.model.classifier = torch.nn.Sequential(
+            torch.nn.Linear(nf, 256),
+            torch.nn.ReLU(inplace=True),
+            torch.nn.Dropout(0.5),
+            torch.nn.Linear(256, len(self.labels))
+        )
+        self.model.load_state_dict(torch.load(f"{path}/model.safetensors", map_location="cpu"))
+        self.model.eval()
+        self.trans = T.Compose([
+            T.Resize((224, 224)),
+            T.ToTensor(),
+            T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
+        ])
+
+    def __call__(self, data: Dict[str, Any]):
+        img_bytes = data['inputs']
+        if isinstance(img_bytes, str):  # base64
+            img_bytes = base64.b64decode(img_bytes)
+        img = Image.open(io.BytesIO(img_bytes)).convert('RGB')
+        x = self.trans(img).unsqueeze(0)
+        with torch.no_grad():
+            probs = self.model(x).softmax(1)[0]
+        topk = probs.topk(5)
+        return [{'label': self.labels[i], 'score': float(topk.values[j])}
+                for j, i in enumerate(topk.indices)]

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0d1ba4d1966d0cf2eb8143e5c3b64f50d3e2c5484c67036dfcf52936cbb319c
+size 82726304

requirements.txt

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+torch
+torchvision
+pillow
+safetensors