JDATA2019雪豹识别挑战赛冠军方案源码.zip

# GCNet for Object Detection By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). We provide config files to reproduce the results in the paper for ["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. ## Introduction **GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. ## Citing GCNet ``` @article{cao2019GCNet, title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, journal={arXiv preprint arXiv:1904.11492}, year={2019} } ``` ## Results and models The results on COCO 2017val are shown in the below table. | Backbone | Model | Context | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | :-------: | :--------------: | :------------: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-------: | | R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 4.5 | 0.533 | 10.1 | 38.5 | 35.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x_20190602-c550c707.pth) | | R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 4.6 | 0.533 | 9.9 | 38.9 | 35.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x_20190602-18ae2dfd.pth) | | R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.0 | 0.731 | 8.6 | 40.8 | 37.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_r101_fpn_1x_20190602-f4456442.pth) | | R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.1 | 0.747 | 8.6 | 40.8 | 36.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r4_gcb_c3-c5_r101_fpn_1x_20190602-1ee20d5f.pth) | | Backbone | Model | Context | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | :-------: | :--------------: | :------------: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-------: | | R-50-FPN | Mask | - | 1x | 3.9 | 0.543 | 10.2 | 37.2 | 33.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r50_fpn_syncbn_1x_20190602-bccc62fa.pth) | | R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 4.5 | 0.547 | 9.9 | 39.4 | 35.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x_20190602-a0169c20.pth) | | R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 4.6 | 0.603 | 9.4 | 39.9 | 36.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x_20190602-ace08792.pth) | | R-101-FPN | Mask | - | 1x | 5.8 | 0.665 | 9.2 | 39.8 | 36.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r101_fpn_syncbn_1x_20190602-b2a0e2b7.pth) | | R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.0 | 0.778 | 9.0 | 41.1 | 37.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_r101_fpn_syncbn_1x_20190602-717e6dbd.pth) | | R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.1 | 0.786 | 8.9 | 41.7 | 37.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r4_gcb_c3-c5_r101_fpn_syncbn_1x_20190602-a893c718.pth) | | X-101-FPN | Mask | - | 1x | 7.1 | 0.912 | 8.5 | 41.2 | 37.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn_1x_20190602-bb8ae7e5.pth) | | X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.2 | 1.055 | 7.7 | 42.4 | 38.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-c28edb53.pth) | | X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 8.3 | 1.037 | 7.6 | 42.9 | 38.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r4_gcb_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-930b3d51.pth) | | X-101-FPN | Cascade Mask | - | 1x | - | - | - | 44.7 | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn_1x_20190602-63a800fb.pth) | | X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | - | - | - | 45.9 | 39.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_r16_gcb_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-3e168d88.pth) | | X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | - | - | - | 46.5 | 39.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_r4_gcb_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-b579157f.pth) | | X-101-FPN | DCN Cascade Mask | - | 1x | - | - | - | 47.1 | 40.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_dconv_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-9aa8c394.pth) | | X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | - | - | - | 47.9 | 40.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_r16_gcb_dconv_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-b86027a6.pth) | | X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | - | - | - | 47.9 | 40.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/cascade_mask_rcnn_r4_gcb_dconv_c3-c5_x101_32x4d_fpn_syncbn_1x_20190602-b4164f6b.pth) | **Notes:** - The `SyncBN` is added in the backbone for all models in **Table 2**. - `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. - `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. - `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively.