v0.24.1(31/10/2022) New Features - Support mmcls with NPU backend. ([#1072](https://github.com/open-mmlab/mmclassification/pull/1072)) Bug Fixes - Fix performance issue in convnext DDP train. ([#1098](https://github.com/open-mmlab/mmclassification/pull/1098))

v0.25.0(06/12/2022) Highlights - Support MLU backend. New Features - Support MLU backend. ([#1159](https://github.com/open-mmlab/mmclassification/pull/1159)) - Support Activation Checkpointing for ConvNeXt. ([#1152](https://github.com/open-mmlab/mmclassification/pull/1152)) Improvements - Add `dist_train_arm.sh` for ARM device and update NPU results. ([#1218](https://github.com/open-mmlab/mmclassification/pull/1218)) Bug Fixes - Fix a bug caused `MMClsWandbHook` stuck. ([#1242](https://github.com/open-mmlab/mmclassification/pull/1242)) - Fix the redundant `device_ids` in `tools/test.py`. ([#1215](https://github.com/open-mmlab/mmclassification/pull/1215)) Docs Update - Add version banner and version warning in master docs. ([#1216](https://github.com/open-mmlab/mmclassification/pull/1216)) - Update NPU support doc. ([#1198](https://github.com/open-mmlab/mmclassification/pull/1198)) - Fixed typo in `pytorch2torchscript.md`. ([#1173](https://github.com/open-mmlab/mmclassification/pull/1173)) - Fix typo in `miscellaneous.md`. ([#1137](https://github.com/open-mmlab/mmclassification/pull/1137)) - further detail for the doc for `ClassBalancedDataset`. ([#901](https://github.com/open-mmlab/mmclassification/pull/901))

v0.22.1(15/4/2022) New Features - [Feature] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) - [Feature] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) Improvements - [Enhance] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) - [Enhance] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752))

v0.23.0(1/5/2022) New Features - Support DenseNet. ([#750](https://github.com/open-mmlab/mmclassification/pull/750)) - Support VAN. ([#739](https://github.com/open-mmlab/mmclassification/pull/739)) Improvements - Support training on IPU and add fine-tuning configs of ViT. ([#723](https://github.com/open-mmlab/mmclassification/pull/723)) Docs Update - New style API reference, and easier to use! Welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). ([#774](https://github.com/open-mmlab/mmclassification/pull/774))

v0.23.1(2/6/2022) New Features - [Feature] Dedicated MMClsWandbHook for MMClassification (Weights and Biases Integration) ([#764](https://github.com/open-mmlab/mmclassification/pull/764)) Improvements - [Refactor] Use mdformat instead of markdownlint to format markdown. ([#844](https://github.com/open-mmlab/mmclassification/pull/844)) Bug Fixes - [Fix] Fix wrong `--local_rank`. Docs Update - [Docs] Update install tutorials. ([#854](https://github.com/open-mmlab/mmclassification/pull/854)) - [Docs] Fix wrong link in README. ([#835](https://github.com/open-mmlab/mmclassification/pull/835))

v0.23.2(28/7/2022) New Features - Support MPS device. ([#894](https://github.com/open-mmlab/mmclassification/pull/894)) Bug Fixes - Fix a bug in Albu which caused crashing. ([#918](https://github.com/open-mmlab/mmclassification/pull/918))

v0.24.0(30/9/2022) Highlights - Support HorNet, EfficientFormerm, SwinTransformer V2 and MViT backbones. - Support Standford Cars dataset. New Features - Support HorNet Backbone. ([#1013](https://github.com/open-mmlab/mmclassification/pull/1013)) - Support EfficientFormer. ([#954](https://github.com/open-mmlab/mmclassification/pull/954)) - Support Stanford Cars dataset. ([#893](https://github.com/open-mmlab/mmclassification/pull/893)) - Support CSRA head. ([#881](https://github.com/open-mmlab/mmclassification/pull/881)) - Support Swin Transform V2. ([#799](https://github.com/open-mmlab/mmclassification/pull/799)) - Support MViT and add checkpoints. ([#924](https://github.com/open-mmlab/mmclassification/pull/924)) Improvements - \[Improve\] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878)) - \[Enhance\] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025)) - \[Enhancement\] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017)) - \[Refactor\] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965)) - \[Improve\] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915)) - \[Improve\] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985)) - \[Improve\] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964)) - \[Improve\] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953)) Bug Fixes - Fix warning with `torch.meshgrid`. ([#860](https://github.com/open-mmlab/mmclassification/pull/860)) - Add matplotlib minimum version requriments. ([#909](https://github.com/open-mmlab/mmclassification/pull/909)) - val loader should not drop last by default. ([#857](https://github.com/open-mmlab/mmclassification/pull/857)) - Fix config.device bug in toturial. ([#1059](https://github.com/open-mmlab/mmclassification/pull/1059)) - Fix attenstion clamp max params ([#1034](https://github.com/open-mmlab/mmclassification/pull/1034)) - Fix device mismatch in Swin-v2. ([#976](https://github.com/open-mmlab/mmclassification/pull/976)) - Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947)) Docs Update - Fix typo in config.md. ([#827](https://github.com/open-mmlab/mmclassification/pull/827)) - Add version for torchvision to avoide error. ([#903](https://github.com/open-mmlab/mmclassification/pull/903)) - Fixed typo for `--out-dir` option of analyze_results.py. ([#898](https://github.com/open-mmlab/mmclassification/pull/898)) - Refine the docstring of RegNet ([#935](https://github.com/open-mmlab/mmclassification/pull/935))

v0.19.0(31/12/2021) Highlights - The feature extraction function has been enhanced. See [#593](https://github.com/open-mmlab/mmclassification/pull/593) for more details. - Provide the high-acc ResNet-50 training settings from [*ResNet strikes back*](https://arxiv.org/abs/2110.00476). - Reproduce the training accuracy of T2T-ViT & RegNetX, and provide self-training checkpoints. - Support DeiT & Conformer backbone and checkpoints. - Provide a CAM visualization tool based on [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam), and detailed [user guide](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#class-activation-map-visualization)! New Features - Support Precise BN. ([#401](https://github.com/open-mmlab/mmclassification/pull/401)) - Add CAM visualization tool. ([#577](https://github.com/open-mmlab/mmclassification/pull/577)) - Repeated Aug and Sampler Registry. ([#588](https://github.com/open-mmlab/mmclassification/pull/588)) - Add DeiT backbone and checkpoints. ([#576](https://github.com/open-mmlab/mmclassification/pull/576)) - Support LAMB optimizer. ([#591](https://github.com/open-mmlab/mmclassification/pull/591)) - Implement the conformer backbone. ([#494](https://github.com/open-mmlab/mmclassification/pull/494)) - Add the frozen function for Swin Transformer model. ([#574](https://github.com/open-mmlab/mmclassification/pull/574)) - Support using checkpoint in Swin Transformer to save memory. ([#557](https://github.com/open-mmlab/mmclassification/pull/557)) Improvements - [Reproduction] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) - [Reproduction] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) - [Enhance] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) - [Enhance] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) - [Enhance] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) - [Enhance] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) - [Enhance] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) - [Enhance] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) - [Refactor] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) - [Refactor] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) - [CI] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) - [CI] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) Bug Fixes - Fix test sampler bug. ([#611](https://github.com/open-mmlab/mmclassification/pull/611)) - Try to create a symbolic link, otherwise copy. ([#580](https://github.com/open-mmlab/mmclassification/pull/580)) - Fix a bug for multiple output in swin transformer. ([#571](https://github.com/open-mmlab/mmclassification/pull/571)) Docs Update - Update mmcv, torch, cuda version in Dockerfile and docs. ([#594](https://github.com/open-mmlab/mmclassification/pull/594)) - Add analysis&misc docs. ([#525](https://github.com/open-mmlab/mmclassification/pull/525)) - Fix docs build dependency. ([#584](https://github.com/open-mmlab/mmclassification/pull/584))

v0.20.0(30/01/2022) Highlights - Support K-fold cross-validation. The tutorial will be released later. - Support HRNet, ConvNeXt, Twins and EfficientNet. - Support model conversion from PyTorch to Core-ML by a tool. New Features - Support K-fold cross-validation. ([#563](https://github.com/open-mmlab/mmclassification/pull/563)) - Support HRNet and add pre-trained models. ([#660](https://github.com/open-mmlab/mmclassification/pull/660)) - Support ConvNeXt and add pre-trained models. ([#670](https://github.com/open-mmlab/mmclassification/pull/670)) - Support Twins and add pre-trained models. ([#642](https://github.com/open-mmlab/mmclassification/pull/642)) - Support EfficientNet and add pre-trained models.([#649](https://github.com/open-mmlab/mmclassification/pull/649)) - Support `features_only` option in `TIMMBackbone`. ([#668](https://github.com/open-mmlab/mmclassification/pull/668)) - Add conversion script from pytorch to Core-ML model. ([#597](https://github.com/open-mmlab/mmclassification/pull/597)) Improvements - New-style CPU training and inference. ([#674](https://github.com/open-mmlab/mmclassification/pull/674)) - Add setup multi-processing both in train and test. ([#671](https://github.com/open-mmlab/mmclassification/pull/671)) - Rewrite channel split operation in ShufflenetV2. ([#632](https://github.com/open-mmlab/mmclassification/pull/632)) - Deprecate the support for "python setup.py test". ([#646](https://github.com/open-mmlab/mmclassification/pull/646)) - Support single-label, softmax, custom eps by asymmetric loss. ([#609](https://github.com/open-mmlab/mmclassification/pull/609)) - Save class names in best checkpoint created by evaluation hook. ([#641](https://github.com/open-mmlab/mmclassification/pull/641)) Bug Fixes - Fix potential unexcepted behaviors if `metric_options` is not specified in multi-label evaluation. ([#647](https://github.com/open-mmlab/mmclassification/pull/647)) - Fix API changes in `pytorch-grad-cam>=1.3.7`. ([#656](https://github.com/open-mmlab/mmclassification/pull/656)) - Fix bug which breaks `cal_train_time` in `analyze_logs.py`. ([#662](https://github.com/open-mmlab/mmclassification/pull/662)) Docs Update - Update README in configs according to OpenMMLab standard. ([#672](https://github.com/open-mmlab/mmclassification/pull/672)) - Update installation guide and README. ([#624](https://github.com/open-mmlab/mmclassification/pull/624))

v0.20.1(07/02/2022) Bug Fixes - Fix the MMCV dependency version.

v0.21.0(04/03/2022) Highlights - Support ResNetV1c and Wide-ResNet, and provide pre-trained models. - Support dynamic input shape for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape. - Reproduce training results of DeiT. And our DeiT-T and DeiT-S have higher accuracy comparing with the official weights. New Features - Add ResNetV1c. ([#692](https://github.com/open-mmlab/mmclassification/pull/692)) - Support Wide-ResNet. ([#715](https://github.com/open-mmlab/mmclassification/pull/715)) - Support gem pooling ([#677](https://github.com/open-mmlab/mmclassification/pull/677)) Improvements - Reproduce training results of DeiT. ([#711](https://github.com/open-mmlab/mmclassification/pull/711)) - Add ConvNeXt pretrain models on ImageNet-1k. ([#707](https://github.com/open-mmlab/mmclassification/pull/707)) - Support dynamic input shape for ViT-based algorithms. ([#706](https://github.com/open-mmlab/mmclassification/pull/706)) - Add `evaluate` function for ConcatDataset. ([#650](https://github.com/open-mmlab/mmclassification/pull/650)) - Enhance vis-pipeline tool. ([#604](https://github.com/open-mmlab/mmclassification/pull/604)) - Return code 1 if scripts runs failed. ([#694](https://github.com/open-mmlab/mmclassification/pull/694)) - Use PyTorch official `one_hot` to implement `convert_to_one_hot`. ([#696](https://github.com/open-mmlab/mmclassification/pull/696)) - Add a new pre-commit-hook to automatically add a copyright. ([#710](https://github.com/open-mmlab/mmclassification/pull/710)) - Add deprecation message for deploy tools. ([#697](https://github.com/open-mmlab/mmclassification/pull/697)) - Upgrade isort pre-commit hooks. ([#687](https://github.com/open-mmlab/mmclassification/pull/687)) - Use `--gpu-id` instead of `--gpu-ids` in non-distributed multi-gpu training/testing. ([#688](https://github.com/open-mmlab/mmclassification/pull/688)) - Remove deprecation. ([#633](https://github.com/open-mmlab/mmclassification/pull/633)) Bug Fixes - Fix Conformer forward with irregular input size. ([#686](https://github.com/open-mmlab/mmclassification/pull/686)) - Add `dist.barrier` to fix a bug in directory checking. ([#666](https://github.com/open-mmlab/mmclassification/pull/666))

v0.22.0(30/3/2022) Highlights - Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. - A new `CustomDataset` class to help you build dataset of yourself! - Support ConvMixer, RepMLP and new dataset - CUB dataset. New Features - [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) - [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) - [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) - [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) - [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) - [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) - [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) - [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) Improvements - [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) - [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) - [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) Bug Fixes - [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) Docs Update - [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) - [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732))

Release v0.16.0(30/9/2021) Highlights - We have improved compatibility with downstream repositories like MMDetection and MMSegmentation. We will add some examples about how to use our backbones in MMDetection. - Add RepVGG backbone and checkpoints. Welcome to use it! - Add timm backbones wrapper, now you can simply use backbones of pytorch-image-models in MMClassification! New Features - Add RepVGG backbone and checkpoints. ([#414](https://github.com/open-mmlab/mmclassification/pull/414)) - Add timm backbones wrapper. ([#427](https://github.com/open-mmlab/mmclassification/pull/427)) Improvements - Fix TnT compatibility and verbose warning. ([#436](https://github.com/open-mmlab/mmclassification/pull/436)) - Support setting `--out-items` in `tools/test.py`. ([#437](https://github.com/open-mmlab/mmclassification/pull/437)) - Add datetime info and saving model using torch<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439)) - Improve downstream repositories compatibility. ([#421](https://github.com/open-mmlab/mmclassification/pull/421)) - Rename the option `--options` to `--cfg-options` in some tools. ([#425](https://github.com/open-mmlab/mmclassification/pull/425)) - Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. ([#422](https://github.com/open-mmlab/mmclassification/pull/422)) Bug Fixes - Fix format error in `test.py` when metric returns `np.ndarray`. ([#441](https://github.com/open-mmlab/mmclassification/pull/441)) - Fix `publish_model` bug if no parent of `out_file`. ([#463](https://github.com/open-mmlab/mmclassification/pull/463)) - Fix num_classes bug in pytorch2onnx.py. ([#458](https://github.com/open-mmlab/mmclassification/pull/458)) - Fix missing runtime requirement `packaging`. ([#459](https://github.com/open-mmlab/mmclassification/pull/459)) - Fix saving simplified model bug in ONNX export tool. ([#438](https://github.com/open-mmlab/mmclassification/pull/438)) Docs Update - Update `getting_started.md` and `install.md`. And rewrite `finetune.md`. ([#466](https://github.com/open-mmlab/mmclassification/pull/466)) - Use PyTorch style docs theme. ([#457](https://github.com/open-mmlab/mmclassification/pull/457)) - Update metafile and Readme. ([#435](https://github.com/open-mmlab/mmclassification/pull/435)) - Add `CITATION.cff`. ([#428](https://github.com/open-mmlab/mmclassification/pull/428))

Highlights - Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use! - Support ImageNet21k dataset. - Add pipeline visualization tools. Try it with the [tutorials](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#pipeline-visualization)! New Features - Add Tokens-to-Token ViT backbone and converted checkpoints. ([#467](https://github.com/open-mmlab/mmclassification/pull/467)) - Add Res2Net backbone and converted weights. ([#465](https://github.com/open-mmlab/mmclassification/pull/465)) - Support ImageNet21k dataset. ([#461](https://github.com/open-mmlab/mmclassification/pull/461)) - Support seesaw loss. ([#500](https://github.com/open-mmlab/mmclassification/pull/500)) - Add pipeline visualization tools. ([#406](https://github.com/open-mmlab/mmclassification/pull/406)) - Add a tool to find broken files. ([#482](https://github.com/open-mmlab/mmclassification/pull/482)) - Add a tool to test TorchServe. ([#468](https://github.com/open-mmlab/mmclassification/pull/468)) Improvements - Refator Vision Transformer. ([#395](https://github.com/open-mmlab/mmclassification/pull/395)) - Use context manager to reuse matplotlib figures. ([#432](https://github.com/open-mmlab/mmclassification/pull/432)) Bug Fixes - Remove `DistSamplerSeedHook` if use `IterBasedRunner`. ([#501](https://github.com/open-mmlab/mmclassification/pull/501)) - Set the priority of `EvalHook` to "LOW" to avoid a bug of `IterBasedRunner`. ([#488](https://github.com/open-mmlab/mmclassification/pull/488)) - Fix a wrong parameter of `get_root_logger` in `apis/train.py`. ([#486](https://github.com/open-mmlab/mmclassification/pull/486)) - Fix version check in dataset builder. ([#474](https://github.com/open-mmlab/mmclassification/pull/474)) Docs Update - Add English Colab tutorials and update Chinese Colab tutorials. ([#483](https://github.com/open-mmlab/mmclassification/pull/483), [#497](https://github.com/open-mmlab/mmclassification/pull/497)) - Add tutuorial for config files. ([#487](https://github.com/open-mmlab/mmclassification/pull/487)) - Add model-pages in Model Zoo. ([#480](https://github.com/open-mmlab/mmclassification/pull/480)) - Add code-spell pre-commit hook and fix a large mount of typos. ([#470](https://github.com/open-mmlab/mmclassification/pull/470))

Highlights - Support MLP-Mixer backbone and provide pre-trained checkpoints. - Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the [tutorial](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#learning-rate-schedule-visualization)! New Features - Add MLP Mixer Backbone. ([#528](https://github.com/open-mmlab/mmclassification/pull/528), [#539](https://github.com/open-mmlab/mmclassification/pull/539)) - Support positive weights in BCE. ([#516](https://github.com/open-mmlab/mmclassification/pull/516)) - Add a tool to visualize learning rate in each iterations. ([#498](https://github.com/open-mmlab/mmclassification/pull/498)) Improvements - Use CircleCI to do unit tests. ([#567](https://github.com/open-mmlab/mmclassification/pull/567)) - Focal loss for single label tasks. ([#548](https://github.com/open-mmlab/mmclassification/pull/548)) - Remove useless `import_modules_from_string`. ([#544](https://github.com/open-mmlab/mmclassification/pull/544)) - Rename config files according to the config name standard. ([#508](https://github.com/open-mmlab/mmclassification/pull/508)) - Use `reset_classifier` to remove head of timm backbones. ([#534](https://github.com/open-mmlab/mmclassification/pull/534)) - Support passing arguments to loss from head. ([#523](https://github.com/open-mmlab/mmclassification/pull/523)) - Refactor `Resize` transform and add `Pad` transform. ([#506](https://github.com/open-mmlab/mmclassification/pull/506)) - Update mmcv dependency version. ([#509](https://github.com/open-mmlab/mmclassification/pull/509)) Bug Fixes - Fix bug when using `ClassBalancedDataset`. ([#555](https://github.com/open-mmlab/mmclassification/pull/555)) - Fix a bug when using iter-based runner with 'val' workflow. ([#542](https://github.com/open-mmlab/mmclassification/pull/542)) - Fix interpolation method checking in `Resize`. ([#547](https://github.com/open-mmlab/mmclassification/pull/547)) - Fix a bug when load checkpoints in mulit-GPUs environment. ([#527](https://github.com/open-mmlab/mmclassification/pull/527)) - Fix an error on indexing scalar metrics in `analyze_result.py`. ([#518](https://github.com/open-mmlab/mmclassification/pull/518)) - Fix wrong condition judgment in `analyze_logs.py` and prevent empty curve. ([#510](https://github.com/open-mmlab/mmclassification/pull/510)) Docs Update - Fix vit config and model broken links. ([#564](https://github.com/open-mmlab/mmclassification/pull/564)) - Add abstract and image for every paper. ([#546](https://github.com/open-mmlab/mmclassification/pull/546)) - Add mmflow and mim in banner and readme. ([#543](https://github.com/open-mmlab/mmclassification/pull/543)) - Add schedule and runtime tutorial docs. ([#499](https://github.com/open-mmlab/mmclassification/pull/499)) - Add the top-5 acc in ResNet-CIFAR README. ([#531](https://github.com/open-mmlab/mmclassification/pull/531)) - Fix TOC of `visualization.md` and add example images. ([#513](https://github.com/open-mmlab/mmclassification/pull/513)) - Use docs link of other projects and add MMCV docs. ([#511](https://github.com/open-mmlab/mmclassification/pull/511))