segmentation

Applying OverLoCK to Semantic Segmentation

1. Requirements

pip install mmcv-full==1.7.2 --no-cache-dir
pip install mmsegmentation==0.30.0 --no-cache-dir

💡 To enable torch>=2.1.0 to support mmcv 1.7.2, you need to make the following changes:

1️⃣ https://goo.su/XhU5vWr
2️⃣ https://goo.su/ogm4yO

2. Data Preparation

Prepare ADE20K dataset according to the guidelines.

3. Main Results on ADE20K using UperNet framework

Backbone	Pretrain	Schedule	mIoU	Config	Download
OverLoCK-T	ImageNet-1K	160K	50.3	config	model
OverLoCK-S	ImageNet-1K	160K	51.3	config	model
OverLoCK-B	ImageNet-1K	160K	51.7	config	model

4. Train

To train OverLoCK-T + UperNet model on ADE20K dataset with 8 gpus (single node), run:

bash scripts/dist_train.sh configs/overlock/upernet_overlock_tiny_ade20k_8xb2.py 8

5. Validation

To evaluate OverLoCK-T + UperNet model on ADE20K dataset, run:

bash scripts/dist_test.sh configs/overlock/upernet_overlock_tiny_ade20k_8xb2.py path-to-checkpoint 8 --eval mIoU

Citation

If you find this project useful for your research, please consider citing:

@inproceedings{lou2025overlock,
  title={OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels},
  author={Lou, Meng and Yu, Yizhou},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={128--138},
  year={2025}
}