## Changelog ### v0.11.1(21/5/2021) - Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243) #### New Features - Add `dim` argument for `GlobalAveragePooling`. (#236) - Add random noise to `RandAugment` magnitude. (#240) - Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243) #### Improvements - Refactor arguments passing for Heads. (#239) - Allow more flexible `magnitude_range` in `RandAugment`. (#249) - Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252) #### Bug Fixes - Fix typo in `analyze_results.py`. (#237) - Fix typo in unittests. (#238) - Check if specified tmpdir exists when testing to avoid deleting existing data. (#242 & #258) - Add missing config files in `MANIFEST.in`. (#250 & #255) - Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251) ### v0.11.0(1/5/2021) - Support cutmix trick. (#198) - Support random augmentation. (#201) - Add `tools/deployment/test.py` as a ONNX runtime test tool. (#212) - Support ViT backbone and add training configs for ViT on ImageNet. (#214) - Add Chinese `README.md` and some Chinese tutorials. (#221) #### New Features - Support cutmix trick. (#198) - Add `simplify` option in `pytorch2onnx.py`. (#200) - Support random augmentation. (#201) - Add config and checkpoint for training ResNet on CIFAR-100. (#208) - Add `tools/deployment/test.py` as a ONNX runtime test tool. (#212) - Support ViT backbone and add training configs for ViT on ImageNet. (#214) - Add finetuning configs for ViT on ImageNet. (#217) - Add `device` option to support training on CPU. (#219) - Add Chinese `README.md` and some Chinese tutorials. (#221) - Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (#225) - Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230) #### Improvements - Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (#203) - Add `cal_acc` option in `ClsHead`. (#206) - Check `CLASSES` in checkpoint to avoid unexpected key error. (#207) - Check mmcv version when importing mmcls to ensure compatibility. (#209) - Update `CONTRIBUTING.md` to align with that in MMCV. (#210) - Change tags to html comments in configs README.md. (#226) - Clean codes in ViT backbone. (#227) - Reformat `pytorch2onnx.md` tutorial. (#229) - Update `setup.py` to support MMCLI. (#232) #### Bug Fixes - Fix missing `cutmix_prob` in ViT configs. (#220) - Fix backend for resize in ResNeXt configs. (#222) ### 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)