chore(models): update for ci and docs

.isort.cfg

@@ -2,7 +2,6 @@
 line_length = 100
 multi_line_output = 3
 balanced_wrapping = True
-# skip = tools/test_net.py, tools/train_net.py
 known_standard_library = setuptools
 known_myself = official
 known_data_processing = cv2,numpy,scipy,PIL,matplotlib

.pylintrc

@@ -3,7 +3,7 @@
 # A comma-separated list of package or module names from where C extensions may
 # be loaded. Extensions are loading into the active Python interpreter and may
 # run arbitrary code.
-extension-pkg-whitelist=['megengine', 'cv2']
+extension-pkg-whitelist=['cv2']
 
 # Add files or directories to the blacklist. They should be base names, not
 # paths.

README.md

@@ -54,7 +54,7 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 
 ### 图像分类
 
-图像分类是计算机视觉的基础任务。许多计算机视觉的其它任务（例如物体检测）都使用了基于图像分类的预训练模型。因此，我们提供了各种在ImageNet上预训练好的分类模型，包括[ResNet](./official/vision/classification/resnet)系列, [shufflenet](./official/vision/classification/shufflenet)系列等，这些模型在**ImageNet验证集**上的测试结果如下表：
+图像分类是计算机视觉的基础任务。许多计算机视觉的其它任务（例如物体检测）都使用了基于图像分类的预训练模型。因此，我们提供了各种在ImageNet上预训练好的分类模型，包括[ResNet](./official/vision/classification/resnet)系列, [Shufflenet](./official/vision/classification/shufflenet)系列等，这些模型在**ImageNet验证集**上的测试结果如下表：
 
 | 模型 | top1 acc | top5 acc |
 | --- | :---: | :---: |
@@ -91,7 +91,7 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 
 ### 图像分割
 
-我们也提供了经典的语义分割模型--[DeepLabV3+](./official/vision/segmentation/)，这个模型在Pascal VOC2012验证集上的测试结果如下：
+我们也提供了经典的语义分割模型[DeepLabV3+](./official/vision/segmentation/)，这个模型在Pascal VOC2012验证集上的测试结果如下：
 
 | 模型                             | mIoU |
 | ---                              | :--: |

official/vision/detection/README.md

@@ -124,7 +124,9 @@ python3 tools/test.py -f configs/retinanet_res50_coco_1x_800size.py -n 8 \
 
 ## 参考文献
 
-- [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312) Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C Lawrence, Lin T Y, Maire M, Belongie S, et al. European conference on computer vision. Springer, Cham, 2014: 740-755.
-- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár. Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988.
-- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497) S. Ren, K. He, R. Girshick, and J. Sun. In: Neural Information Processing Systems(NIPS)(2015).
-- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144) T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.
+- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár. IEEE International Conference on Computer Vision (ICCV), 2017.
+- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497) Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Neural Information Processing Systems (NIPS), 2015.
+- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144) Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan and Serge Belongie. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
+- [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355) Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. IEEE International Conference on Computer Vision (ICCV), 2019.
+- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://arxiv.org/abs/1912.02424) Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z. Li. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
+- [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312) Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. European Conference on Computer Vision (ECCV), 2014.

official/vision/segmentation/README.md

@@ -2,7 +2,7 @@
 
 ## 介绍
 
-本目录包含了采用MegEngine实现的经典[Deeplabv3plus](https://arxiv.org/abs/1802.02611.pdf)网络结构，同时提供了在Pascal VOC2012和Cityscapes数据集上的完整训练和测试代码。
+本目录包含了采用MegEngine实现的经典[DeepLabV3+](https://arxiv.org/abs/1802.02611.pdf)网络结构，同时提供了在Pascal VOC2012和Cityscapes数据集上的完整训练和测试代码。
 
 网络在Pascal VOC2012验证集上的性能和结果如下：
 
@@ -102,5 +102,4 @@ python3 tools/test.py -f configs/deeplabv3plus_res101_voc_512size.py -n 8 \
 
 ## 参考文献
 
-- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611.pdf), Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and
-Hartwig Adam; ECCV, 2018
+- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611) Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. European Conference on Computer Vision (ECCV), 2018.

requirements.txt

@@ -1,3 +1,4 @@
+megengine
 numpy
 opencv-python
 tqdm