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EfficientNet for object detection

This repository implements EfficientNet in the SimpleDet framework. Efficient B5 achives the same mAP with ~1/10 FLOPs compared with ResNet-50.

Qucik Start

# train faster r-cnn with efficientnet fpn backbone
python3 detection_train.py --config config/efficientnet/efficientnet_b5_fpn_bn_scratch_400_6x.py

Results and Models

All AP results are reported on minival of the COCO dataset.

Model InputSize Backbone Train Schedule GPU Image/GPU FP16 Train MEM Train Speed Box AP Link
Faster 400x600 B5-FPN 36 epoch(6X) 8X 1080Ti 8 yes - 75 img/s 37.2 model
Faster 400x600 B5-FPN 54 epoch(9X) 8X 1080Ti 8 yes - 75 img/s 37.9 -
Faster 400x600 B5-FPN 72 epoch(12X) 8X 1080Ti 8 yes - 75 img/s 38.3 -

Reference

@inproceedings{tan2019,
  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  author={Tan, Mingxing and Le, Quoc V},
  booktitle={ICML},
  year={2019}
}