卷积即插即用模块AKConv

# AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters ([preprint](https://arxiv.org/abs/2311.11587)) This repository is a PyTorch implementation of our paper: AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters. # If you are interested in our other work, you can find information on https://github.com/Liuchen1997/RFAConv. The relevant interpolation codes and resampling codes are referenced at https://github.com/dontLoveBugs/Deformable_ConvNet_pytorch. The code has been opened, thank you for your support. # AKConv provides kernels of different sizes for efficient extraction of features.  # Object detection based on COCO2017 and YOLOv5 | Models | AKConv | AP50 | AP75 | AP | APS | APM | APL | GFLOPS | Params (M) | |-----------|--------|------|------|------|------|------|------|--------|------------| | YOLOv5n (Baseline) | - | 45.6 | 28.9 | 27.5 | 13.5 | 31.5 | 35.9 | 4.5 | 1.87 | | | 3 | 47.8 | 31 | 29.8 | 14.5 | 33.2 | 41 | 3.8 | 1.51 | | YOLOv5n | 5 | 48.8 | 32.6 | 31 | 14.6 | 34.1 | 43.2 | 4.1 | 1.65 | | | 9 | 50.5 | 33.9 | 32.3 | 14.9 | 36.1 | 44.1 | 4.8 | 1.94 | | | 13 | 51.2 | 34.5 | 33 | 15.7 | 36.3 | 45.6 | 5.5 | 2.23 | | YOLOv5s (Baseline) | - | 57 | 39.9 | 37.1 | 20.9 | 42.4 | 47.8 | 16.4 | 7.23 | | | 4 | 58.2 | 41.9 | 39.2 | 21.4 | 43.2 | 53.4 | 14.1 | 6.01 | | YOLOv5s | 6 | 59.2 | 42.6 | 39.9 | 21.5 | 44.2 | 54.7 | 15.3 | 6.55 | | | 7 | 59.4 | 43.2 | 40.4 | 21.5 | 44.6 | 55.1 | 15.9 | 6.82 | # Object detection based on VOC 7+12 and YOLOv7 | Models | AKConv | Precision | Recall | mAP50 | mAP | FLOPS | Params | |-------------|--------|-----------|--------|-------|------|-------|--------| | YOLOv7-tiny (Baseline) | - | 77.3 | 69.8 | 76.4 | 50.2 | 13.2 | 6.06 | | | 3 | 80.1 | 68.4 | 76.1 | 50.3 | 12.1 | 5.56 | | | 4 | 78.2 | 70.3 | 76.2 | 50.7 | 12.4 | 5.66 | | YOLOv7-tiny | 5 | 77 | 71.1 | 76.5 | 50.8 | 12.6 | 5.75 | | | 6 | 79.6 | 69.9 | 76.9 | 51 | 12.9 | 5.85 | | | 8 | 78.6 | 70.1 | 76.7 | 51.2 | 13.4 | 6.04 | | | 9 | 81 | 69.3 | 76.7 | 51.3 | 13.7 | 6.14 | # Object detection based on VisDrone-DET2021 and YOLOv5 | Models | AKConv | Precision | Recall | mAP50 | mAP | FLOPS | Params (M) | |---------|--------|-----------|--------|-------|------|-------|------------| | YOLOv5n (Baseline) | - | 38.5 | 28 | 26.4 | 13.4 | 4.2 | 1.77 | | | 3 | 37.9 | 27.4 | 25.9 | 13.2 | 3.5 | 1.41 | | | 5 | 40 | 28 | 26.9 | 13.7 | 3.8 | 1.56 | | | 6 | 38.1 | 28.1 | 26.8 | 13.6 | 4 | 1.63 | | YOLOv5n | 7 | 39.8 | 28.2 | 27.5 | 14.2 | 4.2 | 1.7 | | | 9 | 39.7 | 28.9 | 27.7 | 14.3 | 4.5 | 1.84 | | | 11 | 40.4 | 28.8 | 27.7 | 14.2 | 4.8 | 1.99 | | | 14 | 40 | 28.8 | 27.9 | 14.3 | 5.3 | 2.2 | # Comparison experiments | Models | AP50 | AP75 | AP | APS | APM | APL | GFLOPS | Params (M) | |-------------------------------|------|------|------|------|------|------|--------|------------| | YOLOv5s | 54.8 | 37.5 | 35 | 19.2 | 40 | 45.2 | 16.4 | 7.23 | | YOLOv5s (DSConv =5) | 43.2 | 23.5 | 23.9 | 13.0 | 27.6 | 30.5 | 14.8 | 6.45 | | YOLOv5s (AKConv=5) | 56.6 | 40.7 | 38 | 20.8 | 41.8 | 52 | 14.8 | 6.54 | | YOLOv5s (AKConv=9) | 57.8 | 41.4 | 38.7 | 20.8 | 42.8 | 52.3 | 17.1 | 7.37 | | YOLOv5s (AKConv=9, padding) | 58.3 | 41.9 | 39.2 | 21.6 | 43.2 | 53.5 | 17.1 | 7.37 | | YOLOv5s (Deformable Conv = 3) | 58.5 | 41.8 | 39.1 | 20.8 | 43.4 | 53.6 | 17.1 | 7.37 | | YOLOv5s (AKConv=11) | 58.5 | 42.1 | 39.3 | 21.9 | 43.3 | 53.8 | 18.3 | 7.91 | | YOLOv5s (AKConv=11, padding) | 58.6 | 42.1 | 39.5 | 21.3 | 43.7 | 53.2 | 18.3 | 7.91 | # Comparison experiments | Models | Precision | Recall | mAP50 | mAP | GFLOPS | Params (M) | |--------------------|-----------|--------|-------|------|--------|------------| | YOLOv5n | 73.8 | 62.2 | 68.1 | 41.5 | 4.2 | 1.77 | | YOLOv5n (DSConv=4) | 63 | 50.4 | 54.2 | 26.1 | 3.7 | 1.55 | | YOLOv5n (AKConv=4) | 76.5 | 63.6 | 70.8 | 46.5 | 3.7 | 1.55 | | YOLOv5n (DSConv=9) | 60.6 | 50.8 | 53.4 | 25.3 | 4.8 | 1.9 | | YOLOv5n (AKConv=9) | 76.7 | 65.2 | 71.8 | 48.4 | 4.8 | 1.9 | # Exploring experiments | Models | AP50 | AP75 | AP | APS | APM | APL | GFLOPS | Params (M) | |-------------------|------|------|------|------|------|------|--------|------------| | YOLOv8n | 49.0 | 37.1 | 34.2 | 16.9 | 37.1 | 49.1 | 8.7 | 3.15 | | YOLOv8n-5 (Sampled Shape 1) | 49.5 | 37.6 | 34.9 | 16.8 | 38.2 | 50.2 | 8.4 | 2.94 | | YOLOv8n-5 (Sampled Shape 2) | 49.6 | 37.8 | 34.9 | 15.9 | 38.4 | 50.1 | 8.4 | 2.94 | | YOLOv8n-5 (Sampled Shape 3) | 49.6 | 38.1 | 35 | 16.6 | 38.2 | 50.9 | 8.4 | 2.94 | | YOLOv8n-6 (Sampled Shape 1) | 50.1 | 38.3 | 35.3 | 16.6 | 38.6 | 51.1 | 8.6 | 3.01 | | YOLOv8n-6 (Sampled Shape 2) | 50.2 | 38.2 | 35.4 | 16.6 | 38.3 | 51.3 | 8.6 | 3.01 | | Models |Initial Shape| Precision | Recall | mAP50 | mAP | |-------------------|-----------|-----------|--------|-------|------| | YOLOv5n |a | 39.5 | 27.9 | 26.9 | 13.7 | | YOLOv5n |b | 39.4 | 28.2 | 26.8 | 13.6 | | YOLOv5n |c | 37.4 | 27.8 | 26.1 | 13.4 | | YOLOv5n |d | 37.5 | 27 | 25.5 | 12.9 | | YOLOv5n |e | 38.4 | 27.6 | 26.4 | 13.4 | ### Citation # You may want to cite: ``` @inproceedings{dai2017deformable, title={Deformable convolutional networks}, author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen}, booktitle={Proceedings of the IEEE international conference on computer vision}, pages={764--773}, year={2017} } @misc{zhang2023akconv, title={AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters}, author={Xin Zhang and Yingze Song and Tingting Song and Degang Yang and Yichen Ye and Jie Zhou and Liming Zhang}, year={2023}, eprint={2311.11587}, archivePrefix={arXiv}, primaryClass={cs.CV} }