detection

Applying OverLoCK to Object Detection and Instance Segmentation

1. Requirements

pip install mmcv-full==1.7.2 --no-cache-dir
pip install mmdet==2.28.2 --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 COCO 2017 according to the guidelines.

3. Main Results on COCO using Mask R-CNN framework

Backbone	Pretrain	Schedule	AP_b	AP_m	Config	Download
OverLoCK-T	ImageNet-1K	1x	48.3	43.3	config	model
		3x	49.6	43.9	config	model
OverLoCK-S	ImageNet-1K	1x	49.4	44.0	config	model
		3x	51.0	45.0	config	model
OverLoCK-B	ImageNet-1K	1x	49.9	44.4	config	model
		3x	51.4	45.3	config	model

4. Train

To train OverLoCK-T + Mask R-CNN 1x model on COCO dataset with 8 GPUs (single node), run:

NUM_GPUS=8
CONFIG=configs/maskrcnn_overlock/mask_rcnn_overlock_t_in1k_fpn_1x_coco.py
bash scripts/dist_train.sh $CONFIG $NUM_GPUS

5. Validation

To evaluate OverLoCK-T + Mask R-CNN 1x model on COCO dataset, run:

NUM_GPUS=8
CKPT=path-to-checkpoint.pth
CONFIG=configs/maskrcnn_overlock/mask_rcnn_overlock_t_in1k_fpn_1x_coco.py
bash scripts/dist_test.sh $CONFIG $CKPT $NUM_GPUS --eval bbox segm

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}
}