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README.md

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+---
+language: en
+license: mit
+tags:
+- image-classification
+- resnet
+- corrosion
+library_name: pytorch
+pipeline_tag: image-classification
+task_categories:
+- image-classification
+dataset: custom
+---
+
+# Corrosion Classifier (ResNet50)
+
+This repository contains a ResNet50 image classifier trained to detect corrosion types.
+
+## Labels
+- crevice_corrosion
+- erosion_corrosion
+- galvanic_corrosion
+- mic_corrosion
+- no_corrosion
+- pitting_corrosion
+- stress_corrosion
+- under_insulation_corrosion
+- uniform_corrosion
+
+## Usage (PyTorch)
+
+```python
+import torch, json
+from PIL import Image
+from torchvision import transforms
+import timm
+
+# Load labels
+labels = ['crevice_corrosion', 'erosion_corrosion', 'galvanic_corrosion', 'mic_corrosion', 'no_corrosion', 'pitting_corrosion', 'stress_corrosion', 'under_insulation_corrosion', 'uniform_corrosion']
+
+# Create model
+model = timm.create_model('resnet50', pretrained=False, num_classes=len(labels))
+state = torch.load('resnet50-corrosion-classifier-v1.pth', map_location='cpu')
+missing, unexpected = model.load_state_dict(state, strict=False)
+model.eval()
+
+# Preprocess (ImageNet)
+transform = transforms.Compose([
+    transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+img = Image.open('test.jpg').convert('RGB')
+x = transform(img).unsqueeze(0)
+
+with torch.no_grad():
+    logits = model(x)
+    probs = logits.softmax(dim=1).squeeze().tolist()
+
+idx = int(torch.tensor(probs).argmax())
+print(labels[idx], probs[idx])
+```
+
+> Note: This is a **generic PyTorch checkpoint** (`.pth`). The public Inference API on the Hub does **not** execute arbitrary PyTorch code. If you want to call this model via the **Inference API**, you must convert it to a supported library format (e.g. `transformers` image-classification) or use your existing **Space** and call it via the Gradio API. See below.
+
+## Call via your existing Space (recommended now)
+If your Space works, you can call it programmatically using the Gradio JS Client from Node:
+```js
+import { Client, handle_file } from "@gradio/client";
+
+const app = await Client.connect("jacopo22295/RESNET50-CORROSION_CLASSIFIER_V1"); // your Space id
+const res = await fetch("https://example.com/image.jpg");
+const blob = await res.blob();
+const out = await app.predict("/predict", [handle_file(blob)]);
+console.log(out.data);
+```
+
+## Convert to Transformers (optional, to use Inference API)
+If you later want to enable the one-click Inference API, consider exporting to a `transformers` ImageClassification model (e.g. `ResNetForImageClassification`) and pushing weights + `preprocessor_config.json`. This requires a small conversion script.

example_inference.py

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+import torch
+from PIL import Image
+from torchvision import transforms
+import timm, json
+
+labels = [
+  'crevice_corrosion',
+  'erosion_corrosion',
+  'galvanic_corrosion',
+  'mic_corrosion',
+  'no_corrosion',
+  'pitting_corrosion',
+  'stress_corrosion',
+  'under_insulation_corrosion',
+  'uniform_corrosion'
+]
+
+model = timm.create_model('resnet50', pretrained=False, num_classes=len(labels))
+state = torch.load('resnet50-corrosion-classifier-v1.pth', map_location='cpu')
+model.load_state_dict(state, strict=False)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def predict(path):
+    img = Image.open(path).convert('RGB')
+    x = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        probs = model(x).softmax(dim=1).squeeze().tolist()
+    idx = int(torch.tensor(probs).argmax())
+    return labels[idx], probs[idx]
+
+if __name__ == "__main__":
+    import sys
+    print(predict(sys.argv[1] if len(sys.argv)>1 else "test.jpg"))

labels.json

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+[
+  "crevice_corrosion",
+  "erosion_corrosion",
+  "galvanic_corrosion",
+  "mic_corrosion",
+  "no_corrosion",
+  "pitting_corrosion",
+  "stress_corrosion",
+  "under_insulation_corrosion",
+  "uniform_corrosion"
+]

model_config.json

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+{
+  "arch": "resnet50",
+  "num_labels": 9,
+  "labels": {
+    "0": "crevice_corrosion",
+    "1": "erosion_corrosion",
+    "2": "galvanic_corrosion",
+    "3": "mic_corrosion",
+    "4": "no_corrosion",
+    "5": "pitting_corrosion",
+    "6": "stress_corrosion",
+    "7": "under_insulation_corrosion",
+    "8": "uniform_corrosion"
+  },
+  "image_size": 224,
+  "mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "std": [
+    0.229,
+    0.224,
+    0.225
+  ]
+}

preprocessor_config.json

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+{
+  "do_resize": true,
+  "size": 256,
+  "resample": "bicubic",
+  "do_center_crop": true,
+  "crop_size": 224,
+  "do_normalize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ]
+}

push_to_hub.py

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+# Push this folder to the Hugging Face Model Hub
+# Usage:
+#   pip install huggingface_hub
+#   export HF_TOKEN=hf_xxx  # or run `huggingface-cli login`
+#   python push_to_hub.py --repo jacopo22295/RESNET50-CORROSION_CLASSIFIER_V1
+
+import argparse, os
+from huggingface_hub import HfApi, create_repo, upload_file
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--repo", required=True, help="repo id like user/name")
+    ap.add_argument("--private", action="store_true", help="create as private")
+    args = ap.parse_args()
+
+    api = HfApi()
+    create_repo(args.repo, repo_type="model", exist_ok=True, private=args.private)
+
+    base = os.path.dirname(__file__)
+    files = [
+        "resnet50-corrosion-classifier-v1.pth",
+        "README.md",
+        "model_config.json",
+        "preprocessor_config.json",
+        "requirements.txt",
+        "labels.json"
+    ]
+
+    for f in files:
+        path = os.path.join(base, f)
+        if not os.path.exists(path):
+            raise FileNotFoundError(path)
+        print("Uploading", f)
+        upload_file(
+            path_or_fileobj=path,
+            path_in_repo=f,
+            repo_id=args.repo,
+            repo_type="model"
+        )
+    print("Done. Visit https://huggingface.co/" + args.repo)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+torch>=2.1
+torchvision>=0.16
+timm>=0.9.16
+Pillow>=9.5

resnet50-corrosion-classifier-v1.pth

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+size 94426479