mmclassification 标签

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v0.23.0

7c5ddb1

2022-05-01 21:58

下载

v0.22.1

29b882d

2022-04-15 20:10

下载

v0.22.0

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))

349ec86

2022-03-31 01:37

下载

v0.21.0

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))

2037260

2022-03-04 16:13

下载

v0.20.1

a7f8e96

2022-02-07 11:46

下载

v0.20.0

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&gt;=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))

e0edffb

2022-01-31 12:00

下载

v0.19.0

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))

7dfc9e4

2021-12-31 12:55

下载

v0.18.0

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))

f6076bf

2021-11-30 19:04

下载

v0.17.0

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))

72cffac

2021-10-29 14:04

下载

v0.16.0

Release v0.16.0(30/9/2021)

Highlights
  - We have improved compatibility with downstream repositories like MMDetection and MMSegmentation. We will add some examples about how to use our backbones in MMDetection.
  - Add RepVGG backbone and checkpoints. Welcome to use it!
  - Add timm backbones wrapper, now you can simply use backbones of pytorch-image-models in MMClassification!

New Features
  - Add RepVGG backbone and checkpoints. ([#414](https://github.com/open-mmlab/mmclassification/pull/414))
  - Add timm backbones wrapper. ([#427](https://github.com/open-mmlab/mmclassification/pull/427))

Improvements
  - Fix TnT compatibility and verbose warning. ([#436](https://github.com/open-mmlab/mmclassification/pull/436))
  - Support setting `--out-items` in `tools/test.py`.  ([#437](https://github.com/open-mmlab/mmclassification/pull/437))
  - Add datetime info and saving model using torch<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439))
  - Improve downstream repositories compatibility. ([#421](https://github.com/open-mmlab/mmclassification/pull/421))
  - Rename the option `--options` to `--cfg-options` in some tools. ([#425](https://github.com/open-mmlab/mmclassification/pull/425))
  - Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. ([#422](https://github.com/open-mmlab/mmclassification/pull/422))

Bug Fixes
  - Fix format error in `test.py` when metric returns `np.ndarray`. ([#441](https://github.com/open-mmlab/mmclassification/pull/441))
  - Fix `publish_model` bug if no parent of `out_file`. ([#463](https://github.com/open-mmlab/mmclassification/pull/463))
  - Fix num_classes bug in pytorch2onnx.py. ([#458](https://github.com/open-mmlab/mmclassification/pull/458))
  - Fix missing runtime requirement `packaging`. ([#459](https://github.com/open-mmlab/mmclassification/pull/459))
  - Fix saving simplified model bug in ONNX export tool. ([#438](https://github.com/open-mmlab/mmclassification/pull/438))

Docs Update
  - Update `getting_started.md` and `install.md`. And rewrite `finetune.md`. ([#466](https://github.com/open-mmlab/mmclassification/pull/466))
  - Use PyTorch style docs theme. ([#457](https://github.com/open-mmlab/mmclassification/pull/457))
  - Update metafile and Readme. ([#435](https://github.com/open-mmlab/mmclassification/pull/435))
  - Add `CITATION.cff`. ([#428](https://github.com/open-mmlab/mmclassification/pull/428))

8308636

2021-09-30 13:12

下载

v0.15.0

a41cb2f

2021-08-31 14:33

下载

v0.14.0

ade7b80

2021-08-04 13:25

下载

v0.13.0