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Changelog (MMPreTrain)

v1.1.0(12/10/2023)

New Features

  • [Feature] Implement of Zero-Shot CLIP Classifier (#1737)

  • [Feature] Add minigpt4 gradio demo and training script. (#1758)

Improvements

  • [Config] New Version of config Adapting MobileNet Algorithm (#1774)

  • [Config] Support DINO self-supervised learning in project (#1756)

  • [Config] New Version of config Adapting Swin Transformer Algorithm (#1780)

  • [Enhance] Add iTPN Supports for Non-three channel image (#1735)

  • [Docs] Update dataset download script from opendatalab to openXlab (#1765)

  • [Docs] Update COCO-Retrieval dataset docs. (#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.

v1.0.2(15/08/2023)

New Features

  • Add MFF (#1725)

  • Support training of BLIP2 (#1700)

Improvements

  • New Version of config Adapting MAE Algorithm (#1750)

  • New Version of config Adapting ConvNeXt Algorithm (#1760)

  • New version of config adapting BeitV2 Algorithm (#1755)

  • Update dataset_prepare.md (#1732)

  • New Version of config Adapting Vision Transformer Algorithm (#1727)

  • Support Infographic VQA dataset and ANLS metric. (#1667)

  • Support IconQA dataset. (#1670)

  • Fix typo MIMHIVIT to MAEHiViT (#1749)

v1.0.1(28/07/2023)

Improvements

  • Add init_cfg with type=’pretrained’ to downstream tasks (#1717

  • Set ‘is_init’ in some multimodal methods (#1718

  • Adapt test cases on Ascend NPU (#1728

  • Add GPU Acceleration Apple silicon mac (#1699

  • BEiT refactor (#1705

Bug Fixes

  • Fix dict update in minigpt4. (#1709

  • Fix nested predict for multi-task prediction (#1716

  • Fix the issue #1711 “GaussianBlur doesn’t work” (#1722

  • Just to correct a typo of ‘target’ (#1655

  • Fix freeze without cls_token in vit (#1693

  • Fix RandomCrop bug (#1706

Docs Update

v1.0.0(04/07/2023)

Highlights

  • Support inference of more multi-modal algorithms, such as LLaVA, MiniGPT-4, Otter, etc.

  • Support around 10 multi-modal datasets!

  • Add iTPN, SparK self-supervised learning algorithms.

  • Provide examples of New Config and DeepSpeed/FSDP.

New Features

  • Transfer shape-bias tool from mmselfsup (#1658)

  • Download dataset by using MIM&OpenDataLab (#1630)

  • Support New Configs (#1639, #1647, #1665)

  • Support Flickr30k Retrieval dataset (#1625)

  • Support SparK (#1531)

  • Support LLaVA (#1652)

  • Support Otter (#1651)

  • Support MiniGPT-4 (#1642)

  • Add support for VizWiz dataset (#1636)

  • Add support for vsr dataset (#1634)

  • Add InternImage Classification project (#1569)

  • Support OCR-VQA dataset (#1621)

  • Support OK-VQA dataset (#1615)

  • Support TextVQA dataset (#1569)

  • Support iTPN and HiViT (#1584)

  • Add retrieval mAP metric (#1552)

  • Support NoCap dataset based on BLIP. (#1582)

  • Add GQA dataset (#1585)

Improvements

  • Update fsdp vit-huge and vit-large config (#1675)

  • Support deepspeed with flexible runner (#1673)

  • Update Otter and LLaVA docs and config. (#1653)

  • Add image_only param of ScienceQA (#1613)

  • Support to use “split” to specify training set/validation (#1535)

Bug Fixes

  • Refactor _prepare_pos_embed in ViT (#1656， #1679)

  • Freeze pre norm in vision transformer (#1672)

  • Fix bug loading IN1k dataset (#1641)

  • Fix sam bug (#1633)

  • Fixed circular import error for new transform (#1609)

  • Update torchvision transform wrapper (#1595)

  • Set default out_type in CAM visualization (#1586)

Docs Update

v1.0.0rc8(22/05/2023)

Highlights

  • Support multiple multi-modal algorithms and inferencers. You can explore these features by the 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)

  • Add ScienceQA Metrics (#1577)

  • Support multiple multi-modal algorithms and inferencers. (#1561)

  • add eva02 backbone (#1450)

  • Support dinov2 backbone (#1522)

  • Support some downstream classification datasets. (#1467)

  • Support GLIP (#1308)

  • Register torchvision transforms into mmpretrain (#1265)

  • Add ViT of SAM (#1476)

Improvements

  • [Refactor] Support to freeze channel reduction and add layer decay function (#1490)

  • [Refactor] Support resizing pos_embed while loading ckpt and format output (#1488)

Bug Fixes

  • Fix scienceqa (#1581)

  • Fix config of beit (#1528)

  • Incorrect stage freeze on RIFormer Model (#1573)

  • Fix ddp bugs caused by out_type. (#1570)

  • Fix multi-task-head loss potential bug (#1530)

  • Support bce loss without batch augmentations (#1525)

  • Fix clip generator init bug (#1518)

  • Fix the bug in binary cross entropy loss (#1499)

Docs Update

  • Update PoolFormer citation to CVPR version (#1505)

  • Refine Inference Doc (#1489)

  • Add doc for usage of confusion matrix (#1513)

  • Update MMagic link (#1517)

  • Fix example_project README (#1575)

  • Add NPU support page (#1481)

  • train cfg: Removed old description (#1473)

  • Fix typo in MultiLabelDataset docstring (#1483)

v1.0.0rc7(07/04/2023)

Highlights

  • Integrated Self-supervised learning algorithms from MMSelfSup, such as MAE, BEiT, etc.

  • Support RIFormer, a simple but effective vision backbone by removing token mixer.

  • Support LeViT, XCiT, ViG and ConvNeXt-V2 backbone.

  • Add t-SNE visualization.

  • Refactor dataset pipeline visualization.

  • Support confusion matrix calculation and plot.

New Features

  • Support RIFormer. (#1453)

  • Support XCiT Backbone. (#1305)

  • Support calculate confusion matrix and plot it. (#1287)

  • Support RetrieverRecall metric & Add ArcFace config (#1316)

  • Add ImageClassificationInferencer. (#1261)

  • Support InShop Dataset (Image Retrieval). (#1019)

  • Support LeViT backbone. (#1238)

  • Support VIG Backbone. (#1304)

  • Support ConvNeXt-V2 backbone. (#1294)

Improvements

  • Use PyTorch official scaled_dot_product_attention to accelerate MultiheadAttention. (#1434)

  • Add ln to vit avg_featmap output (#1447)

  • Update analysis tools and documentations. (#1359)

  • Unify the --out and --dump in tools/test.py. (#1307)

  • Enable to toggle whether Gem Pooling is trainable or not. (#1246)

  • Update registries of mmcls. (#1306)

  • Add metafile fill and validation tools. (#1297)

  • Remove useless EfficientnetV2 config files. (#1300)

Bug Fixes

  • Fix precise bn hook (#1466)

  • Fix retrieval multi gpu bug (#1319)

  • Fix error repvgg-deploy base config path. (#1357)

  • Fix bug in test tools. (#1309)

Docs Update

  • Translate some tools tutorials to Chinese. (#1321)

  • Add Chinese translation for runtime.md. (#1313)

Changelog (MMClassification)

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)

  • [Feature] Support TTA and add --tta in tools/test.py. (#1161)

  • [Feature] Support Multi-task. (#1229)

  • [Feature] Add clip backbone. (#1258)

  • [Feature] Add mixmim backbone with checkpoints. (#1224)

  • [Feature] Add TinyViT for dev-1.x. (#1042)

  • [Feature] Add some scripts for development. (#1257)

  • [Feature] Support EVA. (#1239)

  • [Feature] Implementation of RevViT. (#1127)

Improvements

  • [Reproduce] Reproduce RepVGG Training Accuracy. (#1264)

  • [Enhance] Support ConvNeXt More Weights. (#1240)

  • [Reproduce] Update ConvNeXt config files. (#1256)

  • [CI] Update CI to test PyTorch 1.13.0. (#1260)

  • [Project] Add ACCV workshop 1st Solution. (#1245)

  • [Project] Add Example project. (#1254)

Bug Fixes

  • [Fix] Fix imports in transforms. (#1255)

  • [Fix] Fix CAM visualization. (#1248)

  • [Fix] Fix the requirements and lazy register mmpretrain models. (#1275)

v1.0.0rc4(06/12/2022)

Highlights

  • Upgrade API to get pre-defined models of MMClassification. See #1236 for more details.

  • Refactor BEiT backbone and support v1/v2 inference. See #1144.

New Features

  • Support getting model from the name defined in the model-index file. (#1236)

Improvements

  • Support evaluate on both EMA and non-EMA models. (#1204)

  • Refactor BEiT backbone and support v1/v2 inference. (#1144)

Bug Fixes

  • Fix reparameterize_model.py doesn’t save meta info. (#1221)

  • Fix dict update in BEiT. (#1234)

Docs Update

  • Update install tutorial. (#1223)

  • Update MobileNetv2 & MobileNetv3 readme. (#1222)

  • Add version selection in the banner. (#1217)

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.

  • Add TIMM and HuggingFace wrappers. Now you can train/use models in TIMM/HuggingFace directly, see #1102.

  • Support retrieval tasks, see #1055.

  • Reproduce mobileone training accuracy. See #1191

New Features

  • Add checkpoints from EfficientNets NoisyStudent & L2. (#1122)

  • Migrate CSRA head to 1.x. (#1177)

  • Support RepLKnet backbone. (#1129)

  • Add Switch Recipe Hook. (#1101)

  • Add adan optimizer. (#1180)

  • Support DaViT. (#1105)

  • Support Activation Checkpointing for ConvNeXt. (#1153)

  • Add TIMM and HuggingFace wrappers to build classifiers from them directly. (#1102)

  • Add reduction for neck (#978)

  • Support HorNet Backbone for dev1.x. (#1094)

  • Add arcface head. (#926)

  • Add Base Retriever and Image2Image Retriever for retrieval tasks. (#1055)

  • Support MobileViT backbone. (#1068)

Improvements

  • [Enhance] Enhance ArcFaceClsHead. (#1181)

  • [Refactor] Refactor to use new fileio API in MMEngine. (#1176)

  • [Enhance] Reproduce mobileone training accuracy. (#1191)

  • [Enhance] add deleting params info in swinv2. (#1142)

  • [Enhance] Add more mobilenetv3 pretrains. (#1154)

  • [Enhancement] RepVGG for YOLOX-PAI for dev-1.x. (#1126)

  • [Improve] Speed up data preprocessor. (#1064)

Bug Fixes

  • Fix the torchserve. (#1143)

  • Fix configs due to api refactor of num_classes. (#1184)

  • Update mmpretrain2torchserve. (#1189)

  • Fix for inference_model cannot get classes information in checkpoint. (#1093)

Docs Update

  • Add not-found page extension. (#1207)

  • update visualization doc. (#1160)

  • Support sort and search the Model Summary table. (#1100)

  • Improve the ResNet model page. (#1118)

  • update the readme of convnext. (#1156)

  • Fix the installation docs link in README. (#1164)

  • Improve ViT and MobileViT model pages. (#1155)

  • Improve Swin Doc and Add Tabs enxtation. (#1145)

  • Add MMEval projects link in README. (#1162)

  • Add runtime configuration docs. (#1128)

  • Add custom evaluation docs (#1130)

  • Add custom pipeline docs. (#1124)

  • Add MMYOLO projects link in MMCLS1.x. (#1117)

v1.0.0rc2(12/10/2022)

New Features

  • [Feature] Support DeiT3. (#1065)

Improvements

  • [Enhance] Update analyze_results.py for dev-1.x. (#1071)

  • [Enhance] Get scores from inference api. (#1070)

Bug Fixes

  • [Fix] Update requirements. (#1083)

Docs Update

  • [Docs] Add 1x docs schedule. (#1015)

v1.0.0rc1(30/9/2022)

New Features

  • Support MViT for MMCLS 1.x (#1023)

  • Add ViT huge architecture. (#1049)

  • Support EdgeNeXt for dev-1.x. (#1037)

  • Support Swin Transformer V2 for MMCLS 1.x. (#1029)

  • Add efficientformer Backbone for MMCls 1.x. (#1031)

  • Add MobileOne Backbone For MMCls 1.x. (#1030)

  • Support BEiT Transformer layer. (#919)

Improvements

  • [Refactor] Fix visualization tools. (#1045)

  • [Improve] Update benchmark scripts (#1028)

  • [Improve] Update tools to enable pin_memory and persistent_workers by default. (#1024)

  • [CI] Update circle-ci and github workflow. (#1018)

Bug Fixes

  • Fix verify dataset tool in 1.x. (#1062)

  • Fix loss_weight in LabelSmoothLoss. (#1058)

  • Fix the output position of Swin-Transformer. (#947)

Docs Update

  • Auto generate model summary table. (#1010)

  • Refactor new modules tutorial. (#998)

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, MMClassification 1.x unifies the interfaces of dataset, models, evaluation, and visualization.

And there are some BC-breaking changes. Please check the migration tutorial for more details.

v0.23.1(2/6/2022)

New Features

  • Dedicated MMClsWandbHook for MMClassification (Weights and Biases Integration) (#764)

Improvements

  • Use mdformat instead of markdownlint to format markdown. (#844)

Bug Fixes

  • Fix wrong --local_rank.

Docs Update

  • Update install tutorials. (#854)

  • Fix wrong link in README. (#835)

v0.23.0(1/5/2022)

New Features

  • Support DenseNet. (#750)

  • Support VAN. (#739)

Improvements

  • Support training on IPU and add fine-tuning configs of ViT. (#723)

Docs Update

  • New style API reference, and easier to use! Welcome view it. (#774)

v0.22.1(15/4/2022)

New Features

  • [Feature] Support resize relative position embedding in SwinTransformer. (#749)

  • [Feature] Add PoolFormer backbone and checkpoints. (#746)

Improvements

  • [Enhance] Improve CPE performance by reduce memory copy. (#762)

  • [Enhance] Add extra dataloader settings in configs. (#752)

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)

  • [Feature] Add CustomDataset. (#738)

  • [Feature] Add diff seeds to diff ranks. (#744)

  • [Feature] Support ConvMixer. (#716)

  • [Feature] Our dist_train & dist_test tools support distributed training on multiple machines. (#734)

  • [Feature] Add RepMLP backbone and checkpoints. (#709)

  • [Feature] Support CUB dataset. (#703)

  • [Feature] Support ResizeMix. (#676)

Improvements

  • [Enhance] Use --a-b instead of --a_b in arguments. (#754)

  • [Enhance] Add get_cat_ids and get_gt_labels to KFoldDataset. (#721)

  • [Enhance] Set torch seed in worker_init_fn. (#733)

Bug Fixes

  • [Fix] Fix the discontiguous output feature map of ConvNeXt. (#743)

Docs Update

  • [Docs] Add brief installation steps in README for copy&paste. (#755)

  • [Docs] fix logo url link from mmocr to mmpretrain. (#732)

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)

  • Support Wide-ResNet. (#715)

  • Support gem pooling (#677)

Improvements

  • Reproduce training results of DeiT. (#711)

  • Add ConvNeXt pretrain models on ImageNet-1k. (#707)

  • Support dynamic input shape for ViT-based algorithms. (#706)

  • Add evaluate function for ConcatDataset. (#650)

  • Enhance vis-pipeline tool. (#604)

  • Return code 1 if scripts runs failed. (#694)

  • Use PyTorch official one_hot to implement convert_to_one_hot. (#696)

  • Add a new pre-commit-hook to automatically add a copyright. (#710)

  • Add deprecation message for deploy tools. (#697)

  • Upgrade isort pre-commit hooks. (#687)

  • Use --gpu-id instead of --gpu-ids in non-distributed multi-gpu training/testing. (#688)

  • Remove deprecation. (#633)

Bug Fixes

  • Fix Conformer forward with irregular input size. (#686)

  • Add dist.barrier to fix a bug in directory checking. (#666)

v0.20.1(07/02/2022)

Bug Fixes

  • Fix the MMCV dependency version.

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)

  • Support HRNet and add pre-trained models. (#660)

  • Support ConvNeXt and add pre-trained models. (#670)

  • Support Twins and add pre-trained models. (#642)

  • Support EfficientNet and add pre-trained models.(#649)

  • Support features_only option in TIMMBackbone. (#668)

  • Add conversion script from pytorch to Core-ML model. (#597)

Improvements

  • New-style CPU training and inference. (#674)

  • Add setup multi-processing both in train and test. (#671)

  • Rewrite channel split operation in ShufflenetV2. (#632)

  • Deprecate the support for “python setup.py test”. (#646)

  • Support single-label, softmax, custom eps by asymmetric loss. (#609)

  • Save class names in best checkpoint created by evaluation hook. (#641)

Bug Fixes

  • Fix potential unexcepted behaviors if metric_options is not specified in multi-label evaluation. (#647)

  • Fix API changes in pytorch-grad-cam>=1.3.7. (#656)

  • Fix bug which breaks cal_train_time in analyze_logs.py. (#662)

Docs Update

  • Update README in configs according to OpenMMLab standard. (#672)

  • Update installation guide and README. (#624)

v0.19.0(31/12/2021)

Highlights

  • The feature extraction function has been enhanced. See #593 for more details.

  • Provide the high-acc ResNet-50 training settings from ResNet strikes back.

  • 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, and detailed user guide!

New Features

  • Support Precise BN. (#401)

  • Add CAM visualization tool. (#577)

  • Repeated Aug and Sampler Registry. (#588)

  • Add DeiT backbone and checkpoints. (#576)

  • Support LAMB optimizer. (#591)

  • Implement the conformer backbone. (#494)

  • Add the frozen function for Swin Transformer model. (#574)

  • Support using checkpoint in Swin Transformer to save memory. (#557)

Improvements

  • [Reproduction] Reproduce RegNetX training accuracy. (#587)

  • [Reproduction] Reproduce training results of T2T-ViT. (#610)

  • [Enhance] Provide high-acc training settings of ResNet. (#572)

  • [Enhance] Set a random seed when the user does not set a seed. (#554)

  • [Enhance] Added NumClassCheckHook and unit tests. (#559)

  • [Enhance] Enhance feature extraction function. (#593)

  • [Enhance] Improve efficiency of precision, recall, f1_score and support. (#595)

  • [Enhance] Improve accuracy calculation performance. (#592)

  • [Refactor] Refactor analysis_log.py. (#529)

  • [Refactor] Use new API of matplotlib to handle blocking input in visualization. (#568)

  • [CI] Cancel previous runs that are not completed. (#583)

  • [CI] Skip build CI if only configs or docs modification. (#575)

Bug Fixes

  • Fix test sampler bug. (#611)

  • Try to create a symbolic link, otherwise copy. (#580)

  • Fix a bug for multiple output in swin transformer. (#571)

Docs Update

  • Update mmcv, torch, cuda version in Dockerfile and docs. (#594)

  • Add analysis&misc docs. (#525)

  • Fix docs build dependency. (#584)

v0.18.0(30/11/2021)

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!

New Features

  • Add MLP Mixer Backbone. (#528, #539)

  • Support positive weights in BCE. (#516)

  • Add a tool to visualize learning rate in each iterations. (#498)

Improvements

  • Use CircleCI to do unit tests. (#567)

  • Focal loss for single label tasks. (#548)

  • Remove useless import_modules_from_string. (#544)

  • Rename config files according to the config name standard. (#508)

  • Use reset_classifier to remove head of timm backbones. (#534)

  • Support passing arguments to loss from head. (#523)

  • Refactor Resize transform and add Pad transform. (#506)

  • Update mmcv dependency version. (#509)

Bug Fixes

  • Fix bug when using ClassBalancedDataset. (#555)

  • Fix a bug when using iter-based runner with ‘val’ workflow. (#542)

  • Fix interpolation method checking in Resize. (#547)

  • Fix a bug when load checkpoints in mulit-GPUs environment. (#527)

  • Fix an error on indexing scalar metrics in analyze_result.py. (#518)

  • Fix wrong condition judgment in analyze_logs.py and prevent empty curve. (#510)

Docs Update

  • Fix vit config and model broken links. (#564)

  • Add abstract and image for every paper. (#546)

  • Add mmflow and mim in banner and readme. (#543)

  • Add schedule and runtime tutorial docs. (#499)

  • Add the top-5 acc in ResNet-CIFAR README. (#531)

  • Fix TOC of visualization.md and add example images. (#513)

  • Use docs link of other projects and add MMCV docs. (#511)

v0.17.0(29/10/2021)

Highlights

  • Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use!

  • Support ImageNet21k dataset.

  • Add a pipeline visualization tool. Try it with the tutorials!

New Features

  • Add Tokens-to-Token ViT backbone and converted checkpoints. (#467)

  • Add Res2Net backbone and converted weights. (#465)

  • Support ImageNet21k dataset. (#461)

  • Support seesaw loss. (#500)

  • Add a pipeline visualization tool. (#406)

  • Add a tool to find broken files. (#482)

  • Add a tool to test TorchServe. (#468)

Improvements

  • Refator Vision Transformer. (#395)

  • Use context manager to reuse matplotlib figures. (#432)

Bug Fixes

  • Remove DistSamplerSeedHook if use IterBasedRunner. (#501)

  • Set the priority of EvalHook to “LOW” to avoid a bug when using IterBasedRunner. (#488)

  • Fix a wrong parameter of get_root_logger in apis/train.py. (#486)

  • Fix version check in dataset builder. (#474)

Docs Update

  • Add English Colab tutorials and update Chinese Colab tutorials. (#483, #497)

  • Add tutuorial for config files. (#487)

  • Add model-pages in Model Zoo. (#480)

  • Add code-spell pre-commit hook and fix a large mount of typos. (#470)

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)

  • Add timm backbones wrapper. (#427)

Improvements

  • Fix TnT compatibility and verbose warning. (#436)

  • Support setting --out-items in tools/test.py. (#437)

  • Add datetime info and saving model using torch<1.6 format. (#439)

  • Improve downstream repositories compatibility. (#421)

  • Rename the option --options to --cfg-options in some tools. (#425)

  • Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. (#422)

Bug Fixes

  • Fix format error in test.py when metric returns np.ndarray. (#441)

  • Fix publish_model bug if no parent of out_file. (#463)

  • Fix num_classes bug in pytorch2onnx.py. (#458)

  • Fix missing runtime requirement packaging. (#459)

  • Fix saving simplified model bug in ONNX export tool. (#438)

Docs Update

  • Update getting_started.md and install.md. And rewrite finetune.md. (#466)

  • Use PyTorch style docs theme. (#457)

  • Update metafile and Readme. (#435)

  • Add CITATION.cff. (#428)

v0.15.0(31/8/2021)

Highlights

  • Support hparams argument in AutoAugment and RandAugment to provide hyperparameters for sub-policies.

  • Support custom squeeze channels in SELayer.

  • Support classwise weight in losses.

New Features

  • Add hparams argument in AutoAugment and RandAugment and some other improvement. (#398)

  • Support classwise weight in losses. (#388)

  • Enhance SELayer to support custom squeeze channels. (#417)

Code Refactor

  • Better result visualization. (#419)

  • Use post_process function to handle pred result processing. (#390)

  • Update digit_version function. (#402)

  • Avoid albumentations to install both opencv and opencv-headless. (#397)

  • Avoid unnecessary listdir when building ImageNet. (#396)

  • Use dynamic mmcv download link in TorchServe dockerfile. (#387)

Docs Improvement

  • Add readme of some algorithms and update meta yml. (#418)

  • Add Copyright information. (#413)

  • Fix typo ‘metirc’. (#411)

  • Update QQ group QR code. (#393)

  • Add PR template and modify issue template. (#380)

v0.14.0(4/8/2021)

Highlights

  • Add transformer-in-transformer backbone and pretrain checkpoints, refers to the paper.

  • Add Chinese colab tutorial.

  • Provide dockerfile to build mmpretrain dev docker image.

New Features

  • Add transformer in transformer backbone and pretrain checkpoints. (#339)

  • Support mim, welcome to use mim to manage your mmpretrain project. (#376)

  • Add Dockerfile. (#365)

  • Add ResNeSt configs. (#332)

Improvements

  • Use the presistent_works option if available, to accelerate training. (#349)

  • Add Chinese ipynb tutorial. (#306)

  • Refactor unit tests. (#321)

  • Support to test mmdet inference with mmpretrain backbone. (#343)

  • Use zero as default value of thrs in metrics. (#341)

Bug Fixes

  • Fix ImageNet dataset annotation file parse bug. (#370)

  • Fix docstring typo and init bug in ShuffleNetV1. (#374)

  • Use local ATTENTION registry to avoid conflict with other repositories. (#376)

  • Fix swin transformer config bug. (#355)

  • Fix patch_cfg argument bug in SwinTransformer. (#368)

  • Fix duplicate init_weights call in ViT init function. (#373)

  • Fix broken _base_ link in a resnet config. (#361)

  • Fix vgg-19 model link missing. (#363)

v0.13.0(3/7/2021)

  • Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet.

New Features

  • Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271)

  • Add pretained model of RegNetX. (#269)

  • Support adding custom hooks in config file. (#305)

  • Improve and add Chinese translation of CONTRIBUTING.md and all tools tutorials. (#320)

  • Dump config before training. (#282)

  • Add torchscript and torchserve deployment tools. (#279, #284)

Improvements

  • Improve test tools and add some new tools. (#322)

  • Correct MobilenetV3 backbone structure and add pretained models. (#291)

  • Refactor PatchEmbed and HybridEmbed as independent components. (#330)

  • Refactor mixup and cutmix as Augments to support more functions. (#278)

  • Refactor weights initialization method. (#270, #318, #319)

  • Refactor LabelSmoothLoss to support multiple calculation formulas. (#285)

Bug Fixes

  • Fix bug for CPU training. (#286)

  • Fix missing test data when num_imgs can not be evenly divided by num_gpus. (#299)

  • Fix build compatible with pytorch v1.3-1.5. (#301)

  • Fix magnitude_std bug in RandAugment. (#309)

  • Fix bug when samples_per_gpu is 1. (#311)

v0.12.0(3/6/2021)

  • Finish adding Chinese tutorials and build Chinese documentation on readthedocs.

  • Update ResNeXt checkpoints and ResNet checkpoints on CIFAR.

New Features

  • Improve and add Chinese translation of data_pipeline.md and new_modules.md. (#265)

  • Build Chinese translation on readthedocs. (#267)

  • Add an argument efficientnet_style to RandomResizedCrop and CenterCrop. (#268)

Improvements

  • Only allow directory operation when rank==0 when testing. (#258)

  • Fix typo in base_head. (#274)

  • Update ResNeXt checkpoints. (#283)

Bug Fixes

  • Add attribute data.test in MNIST configs. (#264)

  • Download CIFAR/MNIST dataset only on rank 0. (#273)

  • Fix MMCV version compatibility. (#276)

  • Fix CIFAR color channels bug and update checkpoints in model zoo. (#280)

v0.11.1(21/5/2021)

  • Refine new_dataset.md and add Chinese translation of finture.md, new_dataset.md.

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.

  • Support random augmentation.

  • Add tools/deployment/test.py as a ONNX runtime test tool.

  • Support ViT backbone and add training configs for ViT on ImageNet.

  • Add Chinese README.md and some Chinese tutorials.

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 mmpretrain 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 mmpretrain.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)

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