Code for paper Rethinking Training from Scratch for Object Detection.

The ImageNet pre-training initialization is the de-facto standard for object detection. He et al. found it is possible to train detector from scratch(random initialization) while needing a longer training schedule with proper normalization technique. In this paper, we explore to directly pre-training on target dataset for object detection. Under this situation, we discover that the widely adopted large resizing strategy e.g. resize image to (1333, 800) is important for fine-tuning but it's not necessary for pre-training. Specifically, we propose a new training pipeline for object detection that follows `pre-training and fine-tuning', utilizing low resolution images within target dataset to pre-training detector then load it to fine-tuning with high resolution images. With this strategy, we can use batch normalization(BN) with large bath size during pre-training, it's also memory efficient that we can apply it on machine with very limited GPU memory(11G). We call it direct detection pre-training, and also use direct pre-training for short. Experiment results show that direct pre-training accelerates the pre-training phase by more than 11x on COCO dataset while with even +1.8mAP compared to ImageNet pre-training. Besides, we found direct pre-training is also applicable to transformer based backbones e.g. Swin Transformer.

We also provide models are sufficiently trained with longer schedule(3x), could be used for model initialization.

This project mainly reference MMDetection codebase and Swin Transformer.

• Following MMDection installation guide to install mmdetection.
• install NVIDIA apex:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

git clone https://github.com/wxzs5/direct-pretraining.git
cd direct-pretraining

# set dataset path
mkdir data
ln -s path/to/coco_dataset data/

# single-gpu testing
python test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval <EVAL_METRICS>

# multi-gpu testing
./dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval <EVAL_METRICS>

e.g.

./dist_test.sh configs/direct_pretraining/mask_rcnn_r50_fpn_direct_finetune_p1x_1x_coco.py model.pth 8 --eval bbox segm

• pre-training:

./dist_train.sh <CONFIG_FILE> <GPU_NUM>

• fine-tuning:

# set config file *load_from* the pre-trained model path
./dist_train.sh <CONFIG_FILE> <GPU_NUM>

if this paper helps you, please consider citing direct pre-training.

@article{yang2021Rethink,
  title={Rethinking Training from Scratch for Object Detection},
  author={Yang, Li and Hong, Zhang and Yu, Zhang},
  journal={arXiv preprint arXiv:2106.03112},
  year={2021}
}