Dynamic Label Assignment for Object Detection by Combining Predicted and Anchor IoUs

Introduction

Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize a fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple and effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher quality predicted boxes are selected as positives. Our paper is available at link.

Approach

Installation

The implementation of our algorithm is based on ATSS. Please check ATSS and INSTALL.md for more installation instructions.

Inference

The inference command line on coco minival split:

python tools/test_net.py \
    --config-file configs/dynamic_atss/dynamic_atss_R_50_FPN_1x.yaml \
    MODEL.WEIGHT Dynamic_ATSS_R_50_FPN_1x.pth \
    TEST.IMS_PER_BATCH 4    

Please note that:

1. If your model's name is different, please replace Dynamic_ATSS_R_50_FPN_1x.pth with your own.
2. If you enounter out-of-memory error, please try to reduce TEST.IMS_PER_BATCH to 1.
3. If you want to evaluate a different model, please change --config-file to its config file (in configs/dynamic_atss) and MODEL.WEIGHT to its weights file.

Training

The following command line will train Dynamic_ATSS_R_50_FPN_1x on 8 GPUs with Synchronous Stochastic Gradient Descent (SGD):

python -m torch.distributed.launch \
    --nproc_per_node=8 \
    --master_port=$((RANDOM + 10000)) \
    tools/train_net.py \
    --config-file configs/dynamic_atss/dynamic_atss_R_50_FPN_1x.yaml \
    DATALOADER.NUM_WORKERS 2 \
    OUTPUT_DIR training_dir/dynamic_atss_R_50_FPN_1x

Please note that:

1. If you want to use fewer GPUs, please change --nproc_per_node to the number of GPUs. No other settings need to be changed. The total batch size does not depends on nproc_per_node. If you want to change the total batch size, please change SOLVER.IMS_PER_BATCH in configs/dynamic_atss/dynamic_atss_R_50_FPN_1x.yaml.
2. The models will be saved into OUTPUT_DIR.
3. If you want to train Dynamic ATSS with other backbones, please change --config-file.

Citations

@article{zhang2022dynamic,
  title={Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs},
  author={Zhang, Tianxiao and Luo, Bo and Sharda, Ajay and Wang, Guanghui},
  journal={Journal of Imaging},
  volume={8},
  number={7},
  pages={193},
  year={2022},
  publisher={Multidisciplinary Digital Publishing Institute}
}