This repository implements EfficientNet in the SimpleDet framework. Efficient B5 achives the same mAP with ~1/10 FLOPs compared with ResNet-50.
# train faster r-cnn with efficientnet fpn backbone
python3 detection_train.py --config config/efficientnet/efficientnet_b5_fpn_bn_scratch_400_6x.py
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 | - |
@inproceedings{tan2019,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Tan, Mingxing and Le, Quoc V},
booktitle={ICML},
year={2019}
}