README.md

metadata

license: mit
datasets:
  - dhvazquez/mtg_synthetic_cards_semantic_segmentation
language:
  - en

Magic The Gatering Image Semantic Segmentation model.

Demo
Dataset
Source Code

Model Details

• Architecture: lraspp_mobilenet_v3_large
• Input Size: 320x240
• Number of Classes: 2
• Classes: Background (0), Card (1)

Model Files

• card_segmentation.onnx: ONNX format for cross-platform deployment
• card_segmentation_fp16.onnx: ONNX format for cross-platform deployment, fp16 (light model, only 8.1M)
• card_segmentation.pt: TorchScript format for PyTorch deployment
• card_segmentation_state_dict.pth: PyTorch state dict for training/fine-tuning

Input/Output

• Input: RGB image tensor of shape (1, 3, 320, 240)
• Input normalization: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
• Output: Segmentation logits of shape (1, 2, 320, 240)

Usage

See inference_example.py for example usage.

Requirements

• PyTorch >= 1.9.0
• torchvision >= 0.10.0
• onnxruntime (for ONNX inference)
• opencv-python
• numpy
• Pillow