UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale
Official PyTorch implementation of UniConvNet, from the following paper:
UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale.
ICCV 2025.
Yuhao Wang, Wei Xi
Xi'an Jiaotong University
[arXiv]
We propose UniConvNet, a pure ConvNet model constructed entirely from standard ConvNet modules. UniConvNet performs well on both lightweight and large-scale models.
- ImageNet-1K Training Code
- ImageNet-22K Pre-training Code
- ImageNet-1K Fine-tuning Code
- Downstream Transfer (Detection, Segmentation) Code (Coming soon ...)
| name | resolution | acc@1 | #params | FLOPs | model(hugging face) | model(baidu) |
|---|---|---|---|---|---|---|
| UniConvNet-A | 224x224 | 77.0 | 3.4M | 0.589G | model | model |
| UniConvNet-P0 | 224x224 | 79.1 | 5.2M | 0.932G | model | model |
| UniConvNet-P1 | 224x224 | 79.6 | 6.1M | 0.895G | model | model |
| UniConvNet-P2 | 224x224 | 80.5 | 7.6M | 1.25G | model | model |
| UniConvNet-N0 | 224x224 | 81.6 | 10.2M | 1.65G | model | model |
| UniConvNet-N1 | 224x224 | 82.2 | 13.1M | 1.88G | model | model |
| UniConvNet-N2 | 224x224 | 82.7 | 15.0M | 2.47G | model | model |
| UniConvNet-N3 | 224x224 | 83.2 | 19.7M | 3.37G | model | model |
| UniConvNet-T | 224x224 | 84.2 | 30.3M | 5.1G | model | model |
| UniConvNet-T | 384x384 | 85.4 | 30.3M | 15.0G | model | model |
| UniConvNet-S | 224x224 | 84.5 | 50.0M | 8.48G | model | model |
| UniConvNet-S | 384x384 | 85.7 | 50.0M | 24.9G | model | model |
| UniConvNet-B | 224x224 | 85.0 | 97.6M | 15.9G | model | model |
| UniConvNet-B | 384x384 | 85.9 | 97.6M | 46.6G | model | model |
| name | resolution | acc@1 | #params | FLOPs | 22k model (hugging face) |
22k model (baidu) | 1k model (hugging face) |
22k model (baidu) |
|---|---|---|---|---|---|---|---|---|
| ConvNeXt-L | 384x384 | 88.2 | 201.8M | 100.1G | model | model | model | model |
| ConvNeXt-XL | 384x384 | 88.4 | 226.7M | 115.2G | model | model | model | model |
Please check INSTALL.md for installation instructions.
We give an example evaluation command for a ImageNet-1K pre-trained UniConvNet-A:
Single-GPU
python main.py --model UniConvNet_A --eval true \
--resume https://huggingface.co/ai-modelwithcode/UniConvNet/resolve/main/uniconvnet_a_1k_224.pth \
--input_size 224 --drop_path 0.05 \
--data_path /path/to/imagenet-1k
Multi-GPU
python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model UniConvNet_A --eval true \
--resume https://huggingface.co/ai-modelwithcode/UniConvNet/resolve/main/uniconvnet_a_1k_224.pth \
--input_size 224 --drop_path 0.05 \
--data_path /path/to/imagenet-1k
This should give
* Acc@1 77.030 Acc@5 93.364 loss 0.983
- For evaluating other model variants, change
--model,--resume,--input_sizeaccordingly. You can get the url to pre-trained models from the tables above. - Setting model-specific
--drop_pathis not strictly required in evaluation, as theDropPathmodule in timm behaves the same during evaluation; but it is required in training. See TRAINING.md or our paper for the values used for different models.
See TRAINING.md for training and fine-tuning instructions.
This repository is built using the timm library, ConvNeXt and InternImage repositories.
This project is released under the MIT license. Please see the LICENSE file for more information.
If you find this repository helpful, please consider citing:
@Article{wang2025uniconvnet,
author = {Yuhao Wang and Wei Xi},
title = {UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale},
journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2025},
}