更换文档检测模型

paddle_detection/configs/vitdet/README.md

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+# Vision Transformer Detection
+
+## Introduction
+
+- [Context Autoencoder for Self-Supervised Representation Learning](https://arxiv.org/abs/2202.03026)  
+- [Benchmarking Detection Transfer Learning with Vision Transformers](https://arxiv.org/pdf/2111.11429.pdf)  
+
+Object detection is a central downstream task used to
+test if pre-trained network parameters confer benefits, such
+as improved accuracy or training speed. The complexity
+of object detection methods can make this benchmarking
+non-trivial when new architectures, such as Vision Transformer (ViT) models, arrive.
+
+## Model Zoo
+
+| Model | Backbone | Pretrained | Scheduler | Images/GPU  | Box AP | Mask AP | Config | Download |
+|:------:|:--------:|:--------------:|:--------------:|:--------------:|:--------------:|:------:|:------:|:--------:|
+| Cascade RCNN | ViT-base | CAE | 1x | 1 | 52.7 | - | [config](./cascade_rcnn_vit_base_hrfpn_cae_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/cascade_rcnn_vit_base_hrfpn_cae_1x_coco.pdparams) |
+| Cascade RCNN | ViT-large | CAE | 1x | 1 | 55.7 | - | [config](./cascade_rcnn_vit_large_hrfpn_cae_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/cascade_rcnn_vit_large_hrfpn_cae_1x_coco.pdparams) |
+| PP-YOLOE | ViT-base | CAE | 36e | 2 | 52.2 | - | [config](./ppyoloe_vit_base_csppan_cae_36e_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_vit_base_csppan_cae_36e_coco.pdparams) |
+| Mask RCNN | ViT-base | CAE | 1x | 1 | 50.6 | 44.9 | [config](./mask_rcnn_vit_base_hrfpn_cae_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/mask_rcnn_vit_base_hrfpn_cae_1x_coco.pdparams) |
+| Mask RCNN | ViT-large | CAE | 1x | 1 | 54.2 | 47.4 | [config](./mask_rcnn_vit_large_hrfpn_cae_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/mask_rcnn_vit_large_hrfpn_cae_1x_coco.pdparams) |
+
+
+**Notes:**
+- Model is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)
+- Base model is trained on 8x32G V100 GPU, large model on 8x80G A100
+- The `Cascade RCNN` experiments are based on PaddlePaddle 2.2.2
+
+## Citations
+```
+@article{chen2022context,
+  title={Context autoencoder for self-supervised representation learning},
+  author={Chen, Xiaokang and Ding, Mingyu and Wang, Xiaodi and Xin, Ying and Mo, Shentong and Wang, Yunhao and Han, Shumin and Luo, Ping and Zeng, Gang and Wang, Jingdong},
+  journal={arXiv preprint arXiv:2202.03026},
+  year={2022}
+}
+
+@article{DBLP:journals/corr/abs-2111-11429,
+  author    = {Yanghao Li and
+               Saining Xie and
+               Xinlei Chen and
+               Piotr Doll{\'{a}}r and
+               Kaiming He and
+               Ross B. Girshick},
+  title     = {Benchmarking Detection Transfer Learning with Vision Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2111.11429},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2111.11429},
+  eprinttype = {arXiv},
+  eprint    = {2111.11429},
+  timestamp = {Fri, 26 Nov 2021 13:48:43 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-11429.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+@article{Cai_2019,
+   title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
+   ISSN={1939-3539},
+   url={http://dx.doi.org/10.1109/tpami.2019.2956516},
+   DOI={10.1109/tpami.2019.2956516},
+   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
+   author={Cai, Zhaowei and Vasconcelos, Nuno},
+   year={2019},
+   pages={1–1}
+}
+```