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## Changelog
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### v0.10.0(1/4/2021)
- Support AutoAugmentation
- Add tutorials for installation and usage.
#### New Features
- Add `Rotate` pipeline for data augmentation. (#167 )
- Add `Invert` pipeline for data augmentation. (#168 )
- Add `Color` pipeline for data augmentation. (#171 )
- Add `Solarize` and `Posterize` pipeline for data augmentation. (#172 )
- Support fp16 training. (#178 )
- Add tutorials for installation and basic usage of MMClassification.(#176 )
- Support `AutoAugmentation` , `AutoContrast` , `Equalize` , `Contrast` , `Brightness` and `Sharpness` pipelines for data augmentation. (#179 )
#### Improvements
- Support dynamic shape export to onnx. (#175 )
- Release training configs and update model zoo for fp16 (#184 )
- Use MMCV's EvalHook in MMClassification (#182 )
#### Bug Fixes
- Fix wrong naming in vgg config (#181 )
2021-03-01 20:14:53 +08:00
### v0.9.0(1/3/2021)
- Implement mixup trick.
- Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.
#### New Features
- Implement mixup and provide configs of training ResNet50 using mixup. (#160 )
- Add `Shear` pipeline for data augmentation. (#163 )
- Add `Translate` pipeline for data augmentation. (#165 )
- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153 )
#### Improvements
- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (#158 )
- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py` , matching the behavior of other open-mmlab projects. (#162 )
#### Bug Fixes
- Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161 )
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### v0.8.0(31/1/2021)
- Support multi-label task.
- Support more flexible metrics settings.
- Fix bugs.
#### New Features
- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123 )
- Add BCE loss for multi-label task. (#130 )
- Add focal loss for multi-label task. (#131 )
- Support PASCAL VOC 2007 dataset for multi-label task. (#134 )
- Add asymmetric loss for multi-label task. (#132 )
- Add analyze_results.py to select images for success/fail demonstration. (#142 )
- Support new metric that calculates the total number of occurrences of each label. (#143 )
- Support class-wise evaluation results. (#143 )
- Add thresholds in eval_metrics. (#146 )
- Add heads and a baseline config for multilabel task. (#145 )
#### Improvements
- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135 )
- Add tags in README.md. (#137 )
- Fix optional issues in docstring. (#138 )
- Update stat.py to classify papers. (#139 )
- Fix mismatched columns in README.md. (#150 )
- Fix test.py to support more evaluation metrics. (#155 )
#### Bug Fixes
- Fix bug in VGG weight_init. (#140 )
- Fix bug in 2 ResNet configs in which outdated heads were used. (#147 )
- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop` . (#151 )
- Fix missing `meta_keys` in `Collect` . (#149 & #152 )
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### v0.7.0(31/12/2020)
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- Add more evaluation metrics.
- Fix bugs.
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#### New Features
- Remove installation of MMCV from requirements. (#90 )
- Add 3 evaluation metrics: precision, recall and F-1 score. (#93 )
- Allow config override during testing and inference with `--options` . (#91 & #96 )
#### Improvements
- Use `build_runner` to make runners more flexible. (#54 )
- Support to get category ids in `BaseDataset` . (#72 )
- Allow `CLASSES` override during `BaseDateset` initialization. (#85 )
- Allow input image as ndarray during inference. (#87 )
- Optimize MNIST config. (#98 )
- Add config links in model zoo documentation. (#99 )
- Use functions from MMCV to collect environment. (#103 )
- Refactor config files so that they are now categorized by methods. (#116 )
- Add README in config directory. (#117 )
- Add model statistics. (#119 )
- Refactor documentation in consistency with other MM repositories. (#126 )
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#### Bug Fixes
- Add missing `CLASSES` argument to dataset wrappers. (#66 )
- Fix slurm evaluation error during training. (#69 )
- Resolve error caused by shape in `Accuracy` . (#104 )
- Fix bug caused by extremely insufficient data in distributed sampler.(#108 )
- Fix bug in `gpu_ids` in distributed training. (#107 )
- Fix bug caused by extremely insufficient data in collect results during testing (#114 )
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### v0.6.0(11/10/2020)
- Support new method: ResNeSt and VGG.
- Support new dataset: CIFAR10.
- Provide new tools to do model inference, model conversion from pytorch to onnx.
#### New Features
- Add model inference. (#16 )
- Add pytorch2onnx. (#20 )
- Add PIL backend for transform `Resize` . (#21 )
- Add ResNeSt. (#25 )
- Add VGG and its pretained models. (#27 )
- Add CIFAR10 configs and models. (#38 )
- Add albumentations transforms. (#45 )
- Visualize results on image demo. (#58 )
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#### Improvements
- Replace urlretrieve with urlopen in dataset.utils. (#13 )
- Resize image according to its short edge. (#22 )
- Update ShuffleNet config. (#31 )
- Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33 )
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#### Bug Fixes
- Fix init_weights in `shufflenet_v2.py` . (#29 )
- Fix the parameter `size` in test_pipeline. (#30 )
- Fix the parameter in cosine lr schedule. (#32 )
- Fix the convert tools for mobilenet_v2. (#34 )
- Fix crash in CenterCrop transform when image is greyscale (#40 )
- Fix outdated configs. (#53 )
