This repository holds the codes and models for the papers.
Chun-Fu (Richard) Chen, Quanfu Fan, Neil Mallinar, Tom Sercu and Rogerio Feris Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
If you use the codes and models from this repo, please cite our work. Thanks!
@inproceedings{
chen2018biglittle,
title={{Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Neil Mallinar and Tom Sercu and Rogerio Feris},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=HJMHpjC9Ym},
}
- pytorch >= 1.0.0
- tensorboard_logger
- tqdm
Or install requirement via:
pip3 install -r requirement.txt
The training script is mostly borrow from the imagenet example of pytorch/examples with modifications.
Please refer the instructions there to prepare the ImageNet dataset.
Training a bL-ResNeXt-101 (64×4d) (α = 2, β = 4) model with two GPUs (0, 1) and saving logfile the LOGDIR folder
python3 imagenet-train.py --data /path/to/folder -d 101 --basewidth 4 \
--cardinality 64 --backbone_net blresnext --alpha 2 --beta 4 \
--lr_scheduler cosine --logdir LOGDIR --gpu 0,1
After download the models, put in the pretrained folder.
Evaluating the bL-ResNeXt-101 (64×4d) (α = 2, β = 4) model with two GPUs.
python3 imagenet-train.py --data /path/to/folder -d 101 --basewidth 4 \
--cardinality 64 --backbone_net blresnext --alpha 2 --beta 4 --evaluate \
--gpu 0,1 --pretrained
Please feel free to raise issue if you encounter issue when using the pretrained models.
After the submission, we re-train our models on PyTorch with the same setting described in the paper.
Performance of Big-Little Net models (evaluating on a single 224x224 image.)
| Model | Top-1 Error | FLOPs (10^9) |
|---|---|---|
| bLResNet-50 (α = 2, β = 4) | 22.41% | 2.85 |
| bLResNet-101 (α = 2, β = 4) | 21.34% | 3.89 |
| bLResNeXt-50 (32x4d) (α = 2, β = 4) | 21.62% | 3.03 |
| bLResNeXt-101 (32x4d) (α = 2, β = 4) | 20.87% | 4.08 |
| bLResNeXt-101 (64x4d) (α = 2, β = 4) | 20.34% | 7.97 |
| bLSEResNeXt-50 (32x4d) (α = 2, β = 4) | 21.44% | 3.03 |
| bLSEResNeXt-101 (32x4d) (α = 2, β = 4) | 21.04% | 4.08 |