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CAR: Class-aware Regularizations for Semantic Segmentation (ECCV 2022)

PWC

<p align="center"> <img src="intro.png" width=500> </p>

This is an offical implementation of the paper CAR: Class-aware Regularizations for Semantic Segmentation:

@inproceedings{cCAR,
  author = {Ye Huang and Di Kang and Liang Chen and Xuefei Zhe and Wenjing Jia and Linchao Bao and Xiangjian He},
  title = {CAR: Class-aware Regularizations for Semantic Segmentation},
  booktitle = {ECCV},
  year = {2022},
}

News

<b>July-12-2022</b> : From July 12-2022, the compressed tfrecord is used for Pascal Context. Please convert Pascal Context again by following docs (Only for prior users).

<b>July-4-2022</b> : CAR: Class-aware Regularizations for Semantic Segmentation has been accepted by ECCV 2022.

<b>May-7-2022</b> : There were few documentation errors in previous commits and we fixed them today. Sorry for any inconvenience caused.

Get start

  1. Install TensorFlow (≥2.8) + Pillow + tqdm (docs)
  2. Download and perpare the datasets (docs)
  3. Download pretrained backbone weights (docs)
  4. Train (docs)
  5. Tell me if you successfully reproduced our result :)

Model Zoo

Here

Reproduce CAR results

Exactly the same results should be obtained if you are using 8 × NVIDIA V100 (SXM2) with iseg <= 0.04. We verified this on many different machines. Note that, you have to use all GPUs on the machine to avoid a deterministic bug that is still under investigation.

To help verify the exact reproduction process, a training log of ResNet-50 + Self-Attention + CAR is provided in resnet50_sa_car_train_log.md