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Trained on CIFAR-10 (10 classes). Weights saved as a plain PyTorch `state_dict` (`pytorch_model.bin`).
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Architecture is defined in `vit_model.py` (uses `timm`).
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## Usage
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```python
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import torch, json
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from huggingface_hub import hf_hub_download
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import importlib.util
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repo_id = "roylvzn/vit-cifar10"
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# fetch files
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weights_path = hf_hub_download(repo_id, "pytorch_model.bin")
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model_py = hf_hub_download(repo_id, "vit_model.py")
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classes_path = hf_hub_download(repo_id, "classes.json")
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# import vit_model.py dynamically
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spec = importlib.util.spec_from_file_location("vit_model", model_py)
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vm = importlib.util.module_from_spec(spec); spec.loader.exec_module(vm)
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# build model and load weights
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model = vm.ViTModel(num_classes=10, pretrained=False)
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state = torch.load(weights_path, map_location="cpu")
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model.load_state_dict(state)
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model.eval()
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with open(classes_path) as f:
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classes = json.load(f)
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# inference expects 224x224 ImageNet-normalized tensors
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Trained on CIFAR-10 (10 classes). Weights saved as a plain PyTorch `state_dict` (`pytorch_model.bin`).
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Architecture is defined in `vit_model.py` (uses `timm`).
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