[CVPR 2025 Oral] OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels

This is an official PyTorch implementation of "OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels".

Introduction

Top-down attention plays a crucial role in the human vision system, wherein the brain initially obtains a rough overview of a scene to discover salient cues (i.e., overview first), followed by a more careful finer-grained examination (i.e., look closely next). However, modern ConvNets remain confined to a pyramid structure that successively downsamples the feature map for receptive field expansion, neglecting this crucial biomimetic principle. We present OverLoCK, the first pure ConvNet backbone architecture that explicitly incorporates a top-down attention mechanism. Unlike pyramid backbone networks, our design features a branched architecture with three synergistic sub-networks: 1) a Base-Net that encodes low/mid-level features; 2) a lightweight Overview-Net that generates dynamic top-down attention through coarse global context modeling (i.e., overview first); and 3) a robust Focus-Net that performs finer-grained perception guided by top-down attention (i.e., look closely next). To fully unleash the power of top-down attention, we further propose a novel context-mixing dynamic convolution (ContMix) that effectively models long-range dependencies while preserving inherent local inductive biases even when the input resolution increases, addressing critical limitations in existing convolutions. Our OverLoCK exhibits a notable performance improvement over existing methods.

<center> <img src="images/img.jpg" width="70%" height="auto"> </center>

News

• Dec. 25, 2025: To improve inference speed and reduce memory consumption, we provide reparameterized versions of the OverLoCK models with pre-trained weights. These variants achieve identical performance to their original counterparts on ImageNet-1K evaluation. However, if you further fine-tune these reparameterized models, they may yield slightly lower accuracy compared to the original versions. Please choose the model variant during fine-tuning based on memory and accuracy requirements on your side (More Details).

• May. 16, 2025: A plug-and-play implementation of the ContMix Block is now available.

Image Classification

1. Requirements

We highly suggest using our provided dependencies to ensure reproducibility:

# Environments:
cuda==12.1
python==3.10
# Dependencies:
pip install torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu121
pip install natten==0.17.1+torch230cu121 -f https://shi-labs.com/natten/wheels/
pip install timm==0.6.12
pip install mmengine==0.2.0

💡 To accelerate training and inference, we utilize the efficient large-kernel convolution proposed in RepLKNet. Please follow this guideline to install the depthwise_conv2d_implicit_gemm function.

💡 If you encounter network issues during the installation of natten, please download this package and install it locally.

2. Data Preparation

Prepare ImageNet with the following folder structure, you can extract ImageNet by this script.

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

3. Main Results on ImageNet-1K with Pretrained Models

| Models | Input Size | FLOPs (G) | Params (M) | Top-1 (%) | Download | |:-----------:|:----------:|:---------:|:----------:|:----------:|:----------:| | OverLoCK-XT | 224x224 | 2.6 | 16 | 82.7 | model | | OverLoCK-T | 224x224 | 5.5 | 33 | 84.2 | model | | OverLoCK-S | 224x224 | 9.7 | 56 | 84.8 | model | | OverLoCK-B | 224x224 | 16.7 | 95 | 85.1 | model |

4. Train

To train OverLoCK models on ImageNet-1K with 8 gpus (single node), run:

bash scripts/train_xt_model.sh # train OverLoCK-XT
bash scripts/train_t_model.sh  # train OverLoCK-T
bash scripts/train_s_model.sh  # train OverLoCK-S
bash scripts/train_b_model.sh  # train OverLoCK-B

💡If you encounter NaN loss, please delete --native-amp to disable AMP training and resume the checkpoint before the NaN loss occurred.

💡If your GPU memory is insufficient during training, you can enable gradient checkpointing by adding the following arguments: --grad-checkpoint --ckpt-stg 4 0 0 0. If you're still experiencing memory issues, you can increase these values, but be aware that this may slow down training speed.

5. Validation

To evaluate OverLoCK on ImageNet-1K, run:

MODEL=overlock_xt # overlock_{xt, t, s, b}
python3 validate.py \
/path/to/imagenet \
--model $MODEL -b 128 \
--pretrained # or --checkpoint /path/to/checkpoint 

💡 To accelerate inference speed, OverLoCK utilizes Structural Re-parameterization. Please refer to here for a simple usage instruction.

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}
}

Dense Predictions

Object Detection
Semantic Segmentation

Acknowledgment

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

timm, natten, unireplknet, mmcv, mmdet, mmseg

Contact

If you have any questions, please feel free to create issues❓ or contact me 📧.