Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Changelog

v0.25.0(06/12/2022)

Highlights

• Support MLU backend.

New Features

• Support MLU backend. (#1159)

• Support Activation Checkpointing for ConvNeXt. (#1152)

Improvements

• Add dist_train_arm.sh for ARM device and update NPU results. (#1218)

Bug Fixes

• Fix a bug caused MMClsWandbHook stuck. (#1242)

• Fix the redundant device_ids in tools/test.py. (#1215)

Docs Update

• Add version banner and version warning in master docs. (#1216)

• Update NPU support doc. (#1198)

• Fixed typo in pytorch2torchscript.md. (#1173)

• Fix typo in miscellaneous.md. (#1137)

• further detail for the doc for ClassBalancedDataset. (#901)

v0.24.1(31/10/2022)

New Features

• Support mmcls with NPU backend. (#1072)

Bug Fixes

• Fix performance issue in convnext DDP train. (#1098)

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)

• Support EfficientFormer. (#954)

• Support Stanford Cars dataset. (#893)

• Support CSRA head. (#881)

• Support Swin Transform V2. (#799)

• Support MViT and add checkpoints. (#924)

Improvements

• [Improve] replace loop of progressbar in api/test. (#878)

• [Enhance] RepVGG for YOLOX-PAI. (#1025)

• [Enhancement] Update VAN. (#1017)

• [Refactor] Re-write get_sinusoid_encoding from third-party implementation. (#965)

• [Improve] Upgrade onnxsim to v0.4.0. (#915)

• [Improve] Fixed typo in RepVGG. (#985)

• [Improve] Using train_step instead of forward in PreciseBNHook (#964)

• [Improve] Use forward_dummy to calculate FLOPS. (#953)

Bug Fixes

• Fix warning with torch.meshgrid. (#860)

• Add matplotlib minimum version requriments. (#909)

• val loader should not drop last by default. (#857)

• Fix config.device bug in toturial. (#1059)

• Fix attenstion clamp max params (#1034)

• Fix device mismatch in Swin-v2. (#976)

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

Docs Update

• Fix typo in config.md. (#827)

• Add version for torchvision to avoide error. (#903)

• Fixed typo for --out-dir option of analyze_results.py. (#898)

• Refine the docstring of RegNet (#935)

v0.23.2(28/7/2022)

New Features

• Support MPS device. (#894)

Bug Fixes

• Fix a bug in Albu which caused crashing. (#918)

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 mmcls. (#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 mmcls dev docker image.

New Features

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

• Support mim, welcome to use mim to manage your mmcls 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 mmcls 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 mmcls to ensure compatibility. (#209)

• Update CONTRIBUTING.md to align with that in MMCV. (#210)

• Change tags to html comments in configs README.md. (#226)

• Clean codes in ViT backbone. (#227)

• Reformat pytorch2onnx.md tutorial. (#229)

• Update setup.py to support MMCLI. (#232)

Bug Fixes

• Fix missing cutmix_prob in ViT configs. (#220)

• Fix backend for resize in ResNeXt configs. (#222)

v0.10.0(1/4/2021)

• Support AutoAugmentation

• Add tutorials for installation and usage.

New Features

• Add Rotate pipeline for data augmentation. (#167)

• Add Invert pipeline for data augmentation. (#168)

• Add Color pipeline for data augmentation. (#171)

• Add Solarize and Posterize pipeline for data augmentation. (#172)

• Support fp16 training. (#178)

• Add tutorials for installation and basic usage of MMClassification.(#176)

• Support AutoAugmentation, AutoContrast, Equalize, Contrast, Brightness and Sharpness pipelines for data augmentation. (#179)

Improvements

• Support dynamic shape export to onnx. (#175)

• Release training configs and update model zoo for fp16 (#184)

• Use MMCV’s EvalHook in MMClassification (#182)

Bug Fixes

• Fix wrong naming in vgg config (#181)

v0.9.0(1/3/2021)

• Implement mixup trick.

• Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.

New Features

• Implement mixup and provide configs of training ResNet50 using mixup. (#160)

• Add Shear pipeline for data augmentation. (#163)

• Add Translate pipeline for data augmentation. (#165)

• Add tools/onnx2tensorrt.py as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153)

Improvements

• Add --eval-options in tools/test.py to support eval options override, matching the behavior of other open-mmlab projects. (#158)

• Support showing and saving painted results in mmcls.apis.test and tools/test.py, matching the behavior of other open-mmlab projects. (#162)

Bug Fixes

• Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161)

v0.8.0(31/1/2021)

• Support multi-label task.

• Support more flexible metrics settings.

• Fix bugs.

New Features

• Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123)

• Add BCE loss for multi-label task. (#130)

• Add focal loss for multi-label task. (#131)

• Support PASCAL VOC 2007 dataset for multi-label task. (#134)

• Add asymmetric loss for multi-label task. (#132)

• Add analyze_results.py to select images for success/fail demonstration. (#142)

• Support new metric that calculates the total number of occurrences of each label. (#143)

• Support class-wise evaluation results. (#143)

• Add thresholds in eval_metrics. (#146)

• Add heads and a baseline config for multilabel task. (#145)

Improvements

• Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135)

• Add tags in README.md. (#137)

• Fix optional issues in docstring. (#138)

• Update stat.py to classify papers. (#139)

• Fix mismatched columns in README.md. (#150)

• Fix test.py to support more evaluation metrics. (#155)

Bug Fixes

• Fix bug in VGG weight_init. (#140)

• Fix bug in 2 ResNet configs in which outdated heads were used. (#147)

• Fix bug of misordered height and width in RandomCrop and RandomResizedCrop. (#151)

• Fix missing meta_keys in Collect. (#149 & #152)

v0.7.0(31/12/2020)

• Add more evaluation metrics.

• Fix bugs.

New Features

• Remove installation of MMCV from requirements. (#90)

• Add 3 evaluation metrics: precision, recall and F-1 score. (#93)

• Allow config override during testing and inference with --options. (#91 & #96)

Improvements

• Use build_runner to make runners more flexible. (#54)

• Support to get category ids in BaseDataset. (#72)

• Allow CLASSES override during BaseDateset initialization. (#85)

• Allow input image as ndarray during inference. (#87)

• Optimize MNIST config. (#98)

• Add config links in model zoo documentation. (#99)

• Use functions from MMCV to collect environment. (#103)

• Refactor config files so that they are now categorized by methods. (#116)

• Add README in config directory. (#117)

• Add model statistics. (#119)

• Refactor documentation in consistency with other MM repositories. (#126)

Bug Fixes

• Add missing CLASSES argument to dataset wrappers. (#66)

• Fix slurm evaluation error during training. (#69)

• Resolve error caused by shape in Accuracy. (#104)

• Fix bug caused by extremely insufficient data in distributed sampler.(#108)

• Fix bug in gpu_ids in distributed training. (#107)

• Fix bug caused by extremely insufficient data in collect results during testing (#114)

v0.6.0(11/10/2020)

• Support new method: ResNeSt and VGG.

• Support new dataset: CIFAR10.

• Provide new tools to do model inference, model conversion from pytorch to onnx.

New Features

• Add model inference. (#16)

• Add pytorch2onnx. (#20)

• Add PIL backend for transform Resize. (#21)

• Add ResNeSt. (#25)

• Add VGG and its pretained models. (#27)

• Add CIFAR10 configs and models. (#38)

• Add albumentations transforms. (#45)

• Visualize results on image demo. (#58)

Improvements

• Replace urlretrieve with urlopen in dataset.utils. (#13)

• Resize image according to its short edge. (#22)

• Update ShuffleNet config. (#31)

• Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33)

Bug Fixes

• Fix init_weights in shufflenet_v2.py. (#29)

• Fix the parameter size in test_pipeline. (#30)

• Fix the parameter in cosine lr schedule. (#32)

• Fix the convert tools for mobilenet_v2. (#34)

• Fix crash in CenterCrop transform when image is greyscale (#40)

• Fix outdated configs. (#53)