v1.0.2(15/08/2023) New Features - Add MFF ([#1725](https://github.com/open-mmlab/mmpretrain/pull/1725)) - Support training of BLIP2 ([#1700](https://github.com/open-mmlab/mmpretrain/pull/1700)) Improvements - New Version of config Adapting MAE Algorithm ([#1750](https://github.com/open-mmlab/mmpretrain/pull/1750)) - New Version of config Adapting ConvNeXt Algorithm ([#1760](https://github.com/open-mmlab/mmpretrain/pull/1760)) - New version of config adapting BeitV2 Algorithm ([#1755](https://github.com/open-mmlab/mmpretrain/pull/1755)) - Update `dataset_prepare.md` ([#1732](https://github.com/open-mmlab/mmpretrain/pull/1732)) - New Version of `config` Adapting Vision Transformer Algorithm ([#1727](https://github.com/open-mmlab/mmpretrain/pull/1727)) - Support Infographic VQA dataset and ANLS metric. ([#1667](https://github.com/open-mmlab/mmpretrain/pull/1667)) - Support IconQA dataset. ([#1670](https://github.com/open-mmlab/mmpretrain/pull/1670)) - Fix typo MIMHIVIT to MAEHiViT ([#1749](https://github.com/open-mmlab/mmpretrain/pull/1749))

v1.1.0(12/10/2023) New Features - [Feature] Implement of Zero-Shot CLIP Classifier ([#1737](https://github.com/open-mmlab/mmpretrain/pull/1737)) - [Feature] Add minigpt4 gradio demo and training script. ([#1758](https://github.com/open-mmlab/mmpretrain/pull/1758)) Improvements - [Config] New Version of config Adapting MobileNet Algorithm ([#1774](https://github.com/open-mmlab/mmpretrain/pull/1774)) - [Config] Support DINO self-supervised learning in project ([#1756](https://github.com/open-mmlab/mmpretrain/pull/1756)) - [Config] New Version of config Adapting Swin Transformer Algorithm ([#1780](https://github.com/open-mmlab/mmpretrain/pull/1780)) - [Enhance] Add iTPN Supports for Non-three channel image ([#1735](https://github.com/open-mmlab/mmpretrain/pull/1735)) - [Docs] Update dataset download script from opendatalab to openXlab ([#1765](https://github.com/open-mmlab/mmpretrain/pull/1765)) - [Docs] Update COCO-Retrieval dataset docs. ([#1806](https://github.com/open-mmlab/mmpretrain/pull/1806)) Bug Fix - Update `train.py` to compat with new config. - Update OFA module to compat with the latest huggingface. - Fix pipeline bug in ImageRetrievalInferencer.

v1.0.0rc5(30/12/2022) Highlights - Support EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones. - Reproduce the training accuracy of ConvNeXt and RepVGG. - Support multi-task training and testing. - Support Test-time Augmentation. New Features - [Feature] Add EfficientnetV2 Backbone. ([#1253](https://github.com/open-mmlab/mmclassification/pull/1253)) - [Feature] Support TTA and add `--tta` in `tools/test.py`. ([#1161](https://github.com/open-mmlab/mmclassification/pull/1161)) - [Feature] Support Multi-task. ([#1229](https://github.com/open-mmlab/mmclassification/pull/1229)) - [Feature] Add clip backbone. ([#1258](https://github.com/open-mmlab/mmclassification/pull/1258)) - [Feature] Add mixmim backbone with checkpoints. ([#1224](https://github.com/open-mmlab/mmclassification/pull/1224)) - [Feature] Add TinyViT for dev-1.x. ([#1042](https://github.com/open-mmlab/mmclassification/pull/1042)) - [Feature] Add some scripts for development. ([#1257](https://github.com/open-mmlab/mmclassification/pull/1257)) - [Feature] Support EVA. ([#1239](https://github.com/open-mmlab/mmclassification/pull/1239)) - [Feature] Implementation of RevViT. ([#1127](https://github.com/open-mmlab/mmclassification/pull/1127)) Improvements - [Reproduce] Reproduce RepVGG Training Accuracy. ([#1264](https://github.com/open-mmlab/mmclassification/pull/1264)) - [Enhance] Support ConvNeXt More Weights. ([#1240](https://github.com/open-mmlab/mmclassification/pull/1240)) - [Reproduce] Update ConvNeXt config files. ([#1256](https://github.com/open-mmlab/mmclassification/pull/1256)) - [CI] Update CI to test PyTorch 1.13.0. ([#1260](https://github.com/open-mmlab/mmclassification/pull/1260)) - [Project] Add ACCV workshop 1st Solution. ([#1245](https://github.com/open-mmlab/mmclassification/pull/1245)) - [Project] Add Example project. ([#1254](https://github.com/open-mmlab/mmclassification/pull/1254)) Bug Fixes - [Fix] Fix imports in transforms. ([#1255](https://github.com/open-mmlab/mmclassification/pull/1255)) - [Fix] Fix CAM visualization. ([#1248](https://github.com/open-mmlab/mmclassification/pull/1248)) - [Fix] Fix the requirements and lazy register mmcls models. ([#1275](https://github.com/open-mmlab/mmclassification/pull/1275))

v1.0.0rc8(22/05/2023) Highlights - Support multiple multi-modal algorithms and inferencers. You can explore these features by the [gradio demo](https://github.com/open-mmlab/mmpretrain/tree/main/projects/gradio_demo)! - Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones. - Register torchvision transforms into MMPretrain, you can now easily integrate torchvision's data augmentations in MMPretrain. New Features - Support Chinese CLIP. ([#1576](https://github.com/open-mmlab/mmpretrain/pull/1576)) - Add ScienceQA Metrics ([#1577](https://github.com/open-mmlab/mmpretrain/pull/1577)) - Support multiple multi-modal algorithms and inferencers. ([#1561](https://github.com/open-mmlab/mmpretrain/pull/1561)) - add eva02 backbone ([#1450](https://github.com/open-mmlab/mmpretrain/pull/1450)) - Support dinov2 backbone ([#1522](https://github.com/open-mmlab/mmpretrain/pull/1522)) - Support some downstream classification datasets. ([#1467](https://github.com/open-mmlab/mmpretrain/pull/1467)) - Support GLIP ([#1308](https://github.com/open-mmlab/mmpretrain/pull/1308)) - Register torchvision transforms into mmpretrain ([#1265](https://github.com/open-mmlab/mmpretrain/pull/1265)) - Add ViT of SAM ([#1476](https://github.com/open-mmlab/mmpretrain/pull/1476)) Improvements - [Refactor] Support to freeze channel reduction and add layer decay function ([#1490](https://github.com/open-mmlab/mmpretrain/pull/1490)) - [Refactor] Support resizing pos_embed while loading ckpt and format output ([#1488](https://github.com/open-mmlab/mmpretrain/pull/1488)) Bug Fixes - Fix scienceqa ([#1581](https://github.com/open-mmlab/mmpretrain/pull/1581)) - Fix config of beit ([#1528](https://github.com/open-mmlab/mmpretrain/pull/1528)) - Incorrect stage freeze on RIFormer Model ([#1573](https://github.com/open-mmlab/mmpretrain/pull/1573)) - Fix ddp bugs caused by `out_type`. ([#1570](https://github.com/open-mmlab/mmpretrain/pull/1570)) - Fix multi-task-head loss potential bug ([#1530](https://github.com/open-mmlab/mmpretrain/pull/1530)) - Support bce loss without batch augmentations ([#1525](https://github.com/open-mmlab/mmpretrain/pull/1525)) - Fix clip generator init bug ([#1518](https://github.com/open-mmlab/mmpretrain/pull/1518)) - Fix the bug in binary cross entropy loss ([#1499](https://github.com/open-mmlab/mmpretrain/pull/1499)) Docs Update - Update PoolFormer citation to CVPR version ([#1505](https://github.com/open-mmlab/mmpretrain/pull/1505)) - Refine Inference Doc ([#1489](https://github.com/open-mmlab/mmpretrain/pull/1489)) - Add doc for usage of confusion matrix ([#1513](https://github.com/open-mmlab/mmpretrain/pull/1513)) - Update MMagic link ([#1517](https://github.com/open-mmlab/mmpretrain/pull/1517)) - Fix example_project README ([#1575](https://github.com/open-mmlab/mmpretrain/pull/1575)) - Add NPU support page ([#1481](https://github.com/open-mmlab/mmpretrain/pull/1481)) - train cfg: Removed old description ([#1473](https://github.com/open-mmlab/mmpretrain/pull/1473)) - Fix typo in MultiLabelDataset docstring ([#1483](https://github.com/open-mmlab/mmpretrain/pull/1483))

v1.0.1(31/07/2023) Improvements - Add init_cfg with type='pretrained' to downstream tasks ([#1717](https://github.com/open-mmlab/mmpretrain/pull/1717) - Set 'is_init' in some multimodal methods ([#1718](https://github.com/open-mmlab/mmpretrain/pull/1718) - Adapt test cases on Ascend NPU ([#1728](https://github.com/open-mmlab/mmpretrain/pull/1728) - Add GPU Acceleration Apple silicon mac ([#1699](https://github.com/open-mmlab/mmpretrain/pull/1699) - BEiT refactor ([#1705](https://github.com/open-mmlab/mmpretrain/pull/1705) Bug Fixes - Fix dict update in minigpt4. ([#1709](https://github.com/open-mmlab/mmpretrain/pull/1709) - Fix nested predict for multi-task prediction ([#1716](https://github.com/open-mmlab/mmpretrain/pull/1716) - Fix the issue #1711 "GaussianBlur doesn't work" ([#1722](https://github.com/open-mmlab/mmpretrain/pull/1722) - Just to correct a typo of 'target' ([#1655](https://github.com/open-mmlab/mmpretrain/pull/1655) - Fix freeze without cls_token in vit ([#1693](https://github.com/open-mmlab/mmpretrain/pull/1693) - Fix RandomCrop bug ([#1706](https://github.com/open-mmlab/mmpretrain/pull/1706) Docs Update - Fix spelling ([#1689](https://github.com/open-mmlab/mmpretrain/pull/1689)

v1.0.0rc1(30/9/2022) New Features - Support MViT for MMCLS 1.x ([#1023](https://github.com/open-mmlab/mmclassification/pull/1023)) - Add ViT huge architecture. ([#1049](https://github.com/open-mmlab/mmclassification/pull/1049)) - Support EdgeNeXt for dev-1.x. ([#1037](https://github.com/open-mmlab/mmclassification/pull/1037)) - Support Swin Transformer V2 for MMCLS 1.x. ([#1029](https://github.com/open-mmlab/mmclassification/pull/1029)) - Add efficientformer Backbone for MMCls 1.x. ([#1031](https://github.com/open-mmlab/mmclassification/pull/1031)) - Add MobileOne Backbone For MMCls 1.x. ([#1030](https://github.com/open-mmlab/mmclassification/pull/1030)) - Support BEiT Transformer layer. ([#919](https://github.com/open-mmlab/mmclassification/pull/919)) Improvements - \[Refactor\] Fix visualization tools. ([#1045](https://github.com/open-mmlab/mmclassification/pull/1045)) - \[Improve\] Update benchmark scripts ([#1028](https://github.com/open-mmlab/mmclassification/pull/1028)) - \[Improve\] Update tools to enable `pin_memory` and `persistent_workers` by default. ([#1024](https://github.com/open-mmlab/mmclassification/pull/1024)) - \[CI\] Update circle-ci and github workflow. ([#1018](https://github.com/open-mmlab/mmclassification/pull/1018)) Bug Fixes - Fix verify dataset tool in 1.x. ([#1062](https://github.com/open-mmlab/mmclassification/pull/1062)) - Fix `loss_weight` in `LabelSmoothLoss`. ([#1058](https://github.com/open-mmlab/mmclassification/pull/1058)) - Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947)) Docs Update - Auto generate model summary table. ([#1010](https://github.com/open-mmlab/mmclassification/pull/1010)) - Refactor new modules tutorial. ([#998](https://github.com/open-mmlab/mmclassification/pull/998))

v1.0.0rc2(12/10/2022) New Features - Support DeiT3. ([#1065](https://github.com/open-mmlab/mmclassification/pull/1065)) Improvements - Update `analyze_results.py` for dev-1.x. ([#1071](https://github.com/open-mmlab/mmclassification/pull/1071)) - Get scores from inference api. ([#1070](https://github.com/open-mmlab/mmclassification/pull/1070)) Bug Fixes - Update requirements. ([#1083](https://github.com/open-mmlab/mmclassification/pull/1083)) Docs Update - Add 1x docs schedule. ([#1015](https://github.com/open-mmlab/mmclassification/pull/1015))

v1.0.0rc3(21/11/2022) Highlights - Add **Switch Recipe** Hook, Now we can modify training pipeline, mixup and loss settings during training, see [#1101](https://github.com/open-mmlab/mmclassification/pull/1101). - Add **TIMM and HuggingFace** wrappers. Now you can train/use models in TIMM/HuggingFace directly, see [#1102](https://github.com/open-mmlab/mmclassification/pull/1102). - Support **retrieval tasks**, see [#1055](https://github.com/open-mmlab/mmclassification/pull/1055). - Reproduce **mobileone** training accuracy. See [#1191](https://github.com/open-mmlab/mmclassification/pull/1191) New Features - Add checkpoints from EfficientNets NoisyStudent & L2. ([#1122](https://github.com/open-mmlab/mmclassification/pull/1122)) - Migrate CSRA head to 1.x. ([#1177](https://github.com/open-mmlab/mmclassification/pull/1177)) - Support RepLKnet backbone. ([#1129](https://github.com/open-mmlab/mmclassification/pull/1129)) - Add Switch Recipe Hook. ([#1101](https://github.com/open-mmlab/mmclassification/pull/1101)) - Add adan optimizer. ([#1180](https://github.com/open-mmlab/mmclassification/pull/1180)) - Support DaViT. ([#1105](https://github.com/open-mmlab/mmclassification/pull/1105)) - Support Activation Checkpointing for ConvNeXt. ([#1153](https://github.com/open-mmlab/mmclassification/pull/1153)) - Add TIMM and HuggingFace wrappers to build classifiers from them directly. ([#1102](https://github.com/open-mmlab/mmclassification/pull/1102)) - Add reduction for neck ([#978](https://github.com/open-mmlab/mmclassification/pull/978)) - Support HorNet Backbone for dev1.x. ([#1094](https://github.com/open-mmlab/mmclassification/pull/1094)) - Add arcface head. ([#926](https://github.com/open-mmlab/mmclassification/pull/926)) - Add Base Retriever and Image2Image Retriever for retrieval tasks. ([#1055](https://github.com/open-mmlab/mmclassification/pull/1055)) - Support MobileViT backbone. ([#1068](https://github.com/open-mmlab/mmclassification/pull/1068)) Improvements - [Enhance] Enhance ArcFaceClsHead. ([#1181](https://github.com/open-mmlab/mmclassification/pull/1181)) - [Refactor] Refactor to use new fileio API in MMEngine. ([#1176](https://github.com/open-mmlab/mmclassification/pull/1176)) - [Enhance] Reproduce mobileone training accuracy. ([#1191](https://github.com/open-mmlab/mmclassification/pull/1191)) - [Enhance] add deleting params info in swinv2. ([#1142](https://github.com/open-mmlab/mmclassification/pull/1142)) - [Enhance] Add more mobilenetv3 pretrains. ([#1154](https://github.com/open-mmlab/mmclassification/pull/1154)) - [Enhancement] RepVGG for YOLOX-PAI for dev-1.x. ([#1126](https://github.com/open-mmlab/mmclassification/pull/1126)) - [Improve] Speed up data preprocessor. ([#1064](https://github.com/open-mmlab/mmclassification/pull/1064)) Bug Fixes - Fix the torchserve. ([#1143](https://github.com/open-mmlab/mmclassification/pull/1143)) - Fix configs due to api refactor of `num_classes`. ([#1184](https://github.com/open-mmlab/mmclassification/pull/1184)) - Update mmcls2torchserve. ([#1189](https://github.com/open-mmlab/mmclassification/pull/1189)) - Fix for `inference_model` cannot get classes information in checkpoint. ([#1093](https://github.com/open-mmlab/mmclassification/pull/1093)) Docs Update - Add not-found page extension. ([#1207](https://github.com/open-mmlab/mmclassification/pull/1207)) - update visualization doc. ([#1160](https://github.com/open-mmlab/mmclassification/pull/1160)) - Support sort and search the Model Summary table. ([#1100](https://github.com/open-mmlab/mmclassification/pull/1100)) - Improve the ResNet model page. ([#1118](https://github.com/open-mmlab/mmclassification/pull/1118)) - update the readme of convnext. ([#1156](https://github.com/open-mmlab/mmclassification/pull/1156)) - Fix the installation docs link in README. ([#1164](https://github.com/open-mmlab/mmclassification/pull/1164)) - Improve ViT and MobileViT model pages. ([#1155](https://github.com/open-mmlab/mmclassification/pull/1155)) - Improve Swin Doc and Add Tabs enxtation. ([#1145](https://github.com/open-mmlab/mmclassification/pull/1145)) - Add MMEval projects link in README. ([#1162](https://github.com/open-mmlab/mmclassification/pull/1162)) - Add runtime configuration docs. ([#1128](https://github.com/open-mmlab/mmclassification/pull/1128)) - Add custom evaluation docs ([#1130](https://github.com/open-mmlab/mmclassification/pull/1130)) - Add custom pipeline docs. ([#1124](https://github.com/open-mmlab/mmclassification/pull/1124)) - Add MMYOLO projects link in MMCLS1.x. ([#1117](https://github.com/open-mmlab/mmclassification/pull/1117))

v1.0.0rc4(06/12/2022) Highlights - Upgrade API to get pre-defined models of MMClassification. See [#1236](https://github.com/open-mmlab/mmclassification/pull/1236) for more details. - Refactor BEiT backbone and support v1/v2 inference. See [#1144](https://github.com/open-mmlab/mmclassification/pull/1144). New Features - Support getting model from the name defined in the model-index file. ([#1236](https://github.com/open-mmlab/mmclassification/pull/1236)) Improvements - Support evaluate on both EMA and non-EMA models. ([#1204](https://github.com/open-mmlab/mmclassification/pull/1204)) - Refactor BEiT backbone and support v1/v2 inference. ([#1144](https://github.com/open-mmlab/mmclassification/pull/1144)) Bug Fixes - Fix `reparameterize_model.py` doesn't save meta info. ([#1221](https://github.com/open-mmlab/mmclassification/pull/1221)) - Fix dict update in BEiT. ([#1234](https://github.com/open-mmlab/mmclassification/pull/1234)) Docs Update - Update install tutorial. ([#1223](https://github.com/open-mmlab/mmclassification/pull/1223)) - Update MobileNetv2 & MobileNetv3 readme. ([#1222](https://github.com/open-mmlab/mmclassification/pull/1222)) - Add version selection in the banner. ([#1217](https://github.com/open-mmlab/mmclassification/pull/1217))

v1.0.0rc0(31/8/2022) MMClassification 1.0.0rc0 is the first version of MMClassification 1.x, a part of the OpenMMLab 2.0 projects. Built upon the new [training engine](https://github.com/open-mmlab/mmengine), MMClassification 1.x unifies the interfaces of dataset, models, evaluation, and visualization. And there are some BC-breaking changes. Please check [the migration tutorial](https://mmclassification.readthedocs.io/en/1.x/migration.html) for more details.

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))

v0.21.0(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))

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))

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))

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.