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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - mirror-detection
+  - image-segmentation
+  - computer-vision
+  - pytorch
+---
+
+# PMDNet — Progressive Mirror Detection
+
+Pretrained weights for **PMDNet**, the model introduced in the CVPR 2020 paper [*Progressive Mirror Detection*](https://jiaying.link/cvpr2020-pgd/).
+
+## Model Description
+
+PMDNet progressively detects mirror surfaces by leveraging multi-scale contrast cues and relational context.
+
+**Architecture overview:**
+
+- **Backbone** — ResNeXt-101 (32×4d), producing feature maps at four scales.
+- **Contrast Module** (`Contrast_Module_Deep`) — at each scale, dilated convolutions capture local–context differences, then four stacked `Contrast_Block_Deep` units compute pairwise local–context subtractions at two dilation rates. Outputs are aggregated with CBAM (channel + spatial attention).
+- **Relation Attention** (`Relation_Attention` / `RAttention`) — criss-cross attention over rows, columns, and both diagonals, enabling long-range relational reasoning without a full self-attention map.
+- **Decoder** — four transposed-convolution upsampling stages with CBAM refinement produce intermediate saliency predictions (`f4 → f1`), each gated by the previous scale's prediction for progressive focus.
+- **Edge Branch** — extracts edge features from `layer1` fused with high-level `cbam_4` context, producing an explicit edge map.
+- **Refinement** — a single 1×1 conv fuses the original image, all four scale predictions, and the edge map into the final mirror mask.
+
+**Input:** RGB image, resized to 416×416.  
+**Output (eval):** `(f4, f3, f2, f1, edge, final)` — sigmoid-activated predictions at input resolution.  
+Optional CRF post-processing is applied to the final prediction.
+
+## Weights
+
+| File | Size | Description |
+|------|------|-------------|
+| `pmd.pth` | ~414 MB | Full model weights (ResNeXt-101 backbone + decoder) |
+
+## Usage
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+from model.pmd import PMD  # from the official code release
+
+model = PMD()
+model.load_state_dict(torch.load("pmd.pth", map_location="cpu"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((416, 416)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+img = Image.open("your_image.jpg").convert("RGB")
+x = transform(img).unsqueeze(0)
+
+with torch.no_grad():
+    f4, f3, f2, f1, edge, final = model(x)
+# `final` is the mirror mask prediction (values in [0, 1])
+```
+
+Full inference script with CRF post-processing: see [`code_minimal/infer.py`](https://jiaying.link/cvpr2020-pgd/).
+
+## Dataset
+
+Trained on the [PMD dataset](https://huggingface.co/datasets/garrying/PMD) (5,095 training images with mirror masks and edge maps).
+
+## Performance
+
+| Method    | F_β   | MAE   |
+|-----------|-------|-------|
+| EGNet     | 0.672 | 0.087 |
+| MirrorNet | 0.748 | 0.061 |
+| **PMDNet (ours)** | **0.790** | **0.032** |
+
+Evaluated on the PMD test split (571 images).
+
+## License
+
+CC BY-NC 4.0 — non-commercial use only.
+
+## Citation
+
+```bibtex
+@INPROCEEDINGS{PMD:2020,
+   Author    = {Jiaying Lin and Guodong Wang and Rynson W.H. Lau},
+   Title     = {Progressive Mirror Detection},
+   Booktitle = {Proc. CVPR},
+   Year      = {2020}
+}
+```
+
+## Contact
+
+csjylin@gmail.com