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Changelog

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)

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)

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)

v0.7.0(31/12/2020)

  • Add more evaluation metrics.
  • Fix bugs.

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)

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)

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)

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)

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)