garrying/PMD-PMDNet

PMDNet — Progressive Mirror Detection

Pretrained weights for PMDNet, the model introduced in the CVPR 2020 paper Progressive Mirror Detection.

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

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.

Dataset

Trained on the PMD dataset (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

@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