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--- |
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license: mit |
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datasets: |
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- dhvazquez/mtg_synthetic_cards_semantic_segmentation |
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language: |
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- en |
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--- |
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# Magic The Gatering Image Semantic Segmentation model. |
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[Demo](https://huggingface.co/spaces/dhvazquez/mtg_semantic_segmentation) |
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[Dataset](https://huggingface.co/datasets/dhvazquez/mtg_synthetic_cards_semantic_segmentation) |
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[Source Code](https://github.com/diegovazquez/mtg_card_image_segmentation) |
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## Model Details |
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- Architecture: lraspp_mobilenet_v3_large |
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- Input Size: 320x240 |
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- Number of Classes: 2 |
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- Classes: Background (0), Card (1) |
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## Model Files |
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- `card_segmentation.onnx`: ONNX format for cross-platform deployment |
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- `card_segmentation_fp16.onnx`: ONNX format for cross-platform deployment, fp16 (light model, only 8.1M) |
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- `card_segmentation.pt`: TorchScript format for PyTorch deployment |
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- `card_segmentation_state_dict.pth`: PyTorch state dict for training/fine-tuning |
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## Input/Output |
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- Input: RGB image tensor of shape (1, 3, 320, 240) |
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- Input normalization: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
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- Output: Segmentation logits of shape (1, 2, 320, 240) |
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## Usage |
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See `inference_example.py` for example usage. |
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## Requirements |
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- PyTorch >= 1.9.0 |
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- torchvision >= 0.10.0 |
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- onnxruntime (for ONNX inference) |
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- opencv-python |
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- numpy |
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- Pillow |
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