Awesome
Partial Residual Networks
This is the implementation of "Enriching Variety of Layer-wise Learning Information by Gradient Combination" using Darknet framwork.
Our paper will be appear in 2019 ICCV Workshop on Low-Power Computer Vision.
For installing Darknet framework, you can refer to darknet(pjreddie) or darknet(AlexeyAB).
We provide YOLO-v3-tiny-PRN cfg file and COCO pre-trained model. You can use provided files to get following results on COCO test-dev set:
| Model | mAP@0.5 | BFLOPs | # Parameter | GPU FPS | CPU FPS |
|---|---|---|---|---|---|
| YOLO-v3-tiny [1] | 33.1 | 5.571 | 8.86M | 300 | 8 |
| YOLO-v3-tiny-PRN | 33.1 | 3.467 | 4.95M | 370 | 13 |
We also provide cfg file and COCO pre-trained model for morden backbone EfficientNet_b0 [2]. For training this model, you should install darknet(AlexeyAB).
| Model | Size | mAP@0.5 | BFLOPs |
|---|---|---|---|
| EfficientNet_b0-PRN | 416x416 | 45.5 | 3.730 |
| EfficientNet_b0-PRN | 320x320 | 41.0 | 2.208 |
Here we provide some experimental results on COCO test-dev set which are not listed in the paper.
| Model | Size | mAP@0.5 | BFLOPs | # Parameter |
|---|---|---|---|---|
| Pelee [3] | 304x304 | 38.3 | 2.58 | 5.98M |
| Pelee-PRN | 320x320 | 40.9 | 2.39 | 3.16M |
| Pelee-YOLOv3 [1] | 320x320 | 41.4 | 2.99 | 3.91M |
| Pelee-FPN [4] | 320x320 | 41.4 | 2.86 | 3.75M |
| Pelee-PRN-3l | 320x320 | 42.5 | 3.98 | 3.36M |
| mPelee-PRN | 320x320 | 42.7 | 2.82 | 3.81M |
| Model | Size | mAP@0.5 | BFLOPs | # Parameter | GPU FPS | CPU FPS |
|---|---|---|---|---|---|---|
| Pelee-PRN | 416x416 | 45.0 | 4.04 | 3.16M | 111 | 6.0 |
| Pelee-YOLOv3 [1] | 416x416 | 45.3 | 5.06 | 3.91M | 115 | 5.5 |
| Pelee-FPN [4] | 416x416 | 45.7 | 4.84 | 3.75M | 115 | 5.8 |
| Pelee-PRN-3l | 416x416 | 46.3 | 5.03 | 3.36M | ||
| mPelee-PRN | 416x416 | 46.8 | 4.76 | 3.81M | 104 |
Reference
[1] Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
[2] Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv preprint arXiv:1905.11946.
[3] Wang, R. J., Li, X., & Ling, C. X. (2018). Pelee: A real-time object detection system on mobile devices. In Advances in Neural Information Processing Systems (pp. 1963-1972).
[4] Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117-2125).