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Recent Releases of mmpose

mmpose - MMPose v1.3.2 Release Note

v1.3.2 (12/07/2024)

New Features

  • Add center alignments for draw_texts in OpencvBackendVisualizer (#2958)
  • Add wflw2coco (#2961)
  • Support 300VW Dataset (#3005)
  • Add RTMW3D for 3D wholebody pose estimation task (#3037)

Improvements

  • In browse dataset : CombinedDataset element are now browse in turn, and image saved into their dataset name folder (#2985)

Bug Fixes

  • Fix loss computation in MSPNHead (#2993)
  • Fix bug in inferencer (#2966)
  • Make category_id in CocoWholeBodyDataset as numpy.array (#2963)

Documentation

  • Add rtmlib examples (#2923)
  • Fix readthedocs configuration (#2979)
  • Add more detailed comments (#2982)
  • Improve documentation folder structure of ExLPose (#2977)

New Contributors

  • @AntDum made their first contribution in https://github.com/open-mmlab/mmpose/pull/2958
  • @Yanyirong made their first contribution in https://github.com/open-mmlab/mmpose/pull/2961
  • @drazicmartin made their first contribution in https://github.com/open-mmlab/mmpose/pull/2977
  • @KeqiangSun made their first contribution in https://github.com/open-mmlab/mmpose/pull/3005
  • @jitrc made their first contribution in https://github.com/open-mmlab/mmpose/pull/3004
  • @zgjja made their first contribution in https://github.com/open-mmlab/mmpose/pull/2963
  • @jibranbinsaleem made their first contribution in https://github.com/open-mmlab/mmpose/pull/3027
  • @cpunion made their first contribution in https://github.com/open-mmlab/mmpose/pull/3026

- Python
Published by xiexinch almost 2 years ago

mmpose - MMPose v1.3.1 Release Note

Fix the bug when downloading config and checkpoint using mim (see Issue #2918).

- Python
Published by Ben-Louis over 2 years ago

mmpose - MMPose v1.3.0 Release Note

RTMO

We are exited to release RTMO: - RTMO is the first one-stage pose estimation method that achieves both high accuracy and real-time speed. - It performs best on crowded scenes. RTMO achieves 83.8% AP on the CrowdPose test set. - RTMO is easy to run for inference and deployment. It does not require an extra human detector. - Try it online with this demo by choosing rtmo | body. - The paper is available on arXiv.

rtmo

Improved RTMW

We have released additional RTMW models in various sizes:

| Config | Input Size | Whole AP | Whole AR | FLOPS
(G) | | :------------------------------ | :--------: | :------: | :------: | :---------------: | | RTMW-m | 256x192 | 58.2 | 67.3 | 4.3 |
| RTMW-l | 256x192 | 66.0 | 74.6 | 7.9 |
| RTMW-x | 256x192 | 67.2 | 75.2 | 13.1 |
| RTMW-l | 384x288 | 70.1 | 78.0 | 17.7 |
| RTMW-x | 384x288 | 70.2 | 78.1 | 29.3 |

The hand keypoint detection accuracy has been notably improved.

db073d10-aee9-41a5-b697-602aae461558

Pose Anything

We are glad to support the inference for the category-agnostic pose estimation method PoseAnything!

Teaser Figure

You can now specify ANY keypoints you want the model to detect, without needing extra training. Under the project folder: 1. Download the pretrained model 2. Run: python demo.py --support [path_to_support_image] --query [path_to_query_image] --config configs/demo_b.py --checkpoint [path_to_pretrained_ckpt]

  • Thanks to the author of PoseAnything (@orhir) for supporting their excellent work!

New Datasets

We have added support for two new datasets:

(CVPR 2023) ExLPose

ExLPose builds a new dataset of real low-light images with accurate pose labels. It can be helpful on tranining a pose estimation model working under extreme light conditions.

ExLPose

  • Thanks @Yang-Changhui for helping with the integration of ExLPose!
  • This is the task of our OpenMMLabCamp, if you also wish to contribute code to us, feel free to refer to this link to pick up the task!

(ICCV 2023) H3WB

H3WB (Human3.6M 3D WholeBody) extends the Human3.6M dataset with 3D whole-body annotations using the COCO wholebody skeleton. This dataset enables more comprehensive 3D pose analysis and benchmarking for whole-body methods.

H3WB

  • Supported by @xiexinch.

Contributors

@Tau-J @Ben-Louis @xiexinch @Yang-Changhui @orhir @RFYoung @yao5401 @icynic @Jendker @willyfh @jit-a3 @Ginray

- Python
Published by Ben-Louis over 2 years ago

mmpose - MMPose v1.2.0 Release Note

RTMW

We are excited to release the alpha version of RTMW: - The first whole-body pose estimation model with accuracy exceeding 70 AP on COCO-Wholebody benchmark. RTMW-x achieves 70.2 AP. - More accurate hand details for pose-guided image generation, gesture recognition, and human-computer interaction, etc. - Compatible with dw_openpose_full preprocessor in sd-webui-controlnet - Try it online with this demo by choosing wholebody(rtmw). - The technical report will be released soon.

New Algorithms

We are glad to support the following new algorithms:

(ICCVW 2023) DWPose

We are glad to support the two-stage distillation method DWPose, which achieves the new SOTA performance on COCO-WholeBody.

  • Since DWPose is the distilled model of RTMPose, you can directly load the weights of DWPose into RTMPose.
  • DWPose has been supported in sd-webui-controlnet.
  • You can also try DWPose online with this demo by choosing wholebody(dwpose).

Here is a guide to train DWPose:

  1. Train DWPose with the first stage distillation

    python bash tools/dist_train.sh configs/wholebody_2d_keypoint/dwpose/ubody/s1_dis/rtmpose_x_dis_l_coco-ubody-384x288.py 8

  2. Transfer the S1 distillation models into regular models

    ```python

    first stage distillation

    python pthtransfer.py $disckpt $newposeckpt ```

  3. Train DWPose with the second stage distillation python bash tools/dist_train.sh configs/wholebody_2d_keypoint/dwpose/ubody/s2_dis/dwpose_l-ll_coco-ubody-384x288.py 8

  4. Transfer the S2 distillation models into regular models

    ```python

    second stage distillation

    python pthtransfer.py $disckpt $newposeckpt --two_dis ```

  • Thanks @yzd-v for helping with the integration of DWPose!

(ICCV 2023) MotionBERT

MotionBERT is the new SOTA method of Monocular 3D Human Pose Estimation on Human3.6M.

motionbert

You can conviently try MotionBERT via the 3D Human Pose Demo with Inferencer:

python python demo/inferencer_demo.py tests/data/coco/000000000785.jpg \ --pose3d human3d --vis-out-dir vis_results/human3d

  • Supported by @LareinaM

(ICLR 2023) EDPose

We support ED-Pose, an end-to-end framework with Explicit box Detection for multi-person Pose estimation. ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision.

The checkpoint is converted from the official repo. The training of EDPose is not supported yet. It will be supported in the future updates.

You can conviently try EDPose via the 2D Human Pose Demo with Inferencer:

python python demo/inferencer_demo.py tests/data/coco/000000197388.jpg \ --pose2d edpose_res50_8xb2-50e_coco-800x1333 --vis-out-dir vis_results

  • Thanks @LiuYi-Up for helping with the integration of EDPose!
  • This is the task of our OpenMMLabCamp, if you also wish to contribute code to us, feel free to refer to this link to pick up the task!

(ICLR 2022) Uniformer

In projects, we implement a topdown heatmap based human pose estimator, utilizing the approach outlined in UniFormer: Unifying Convolution and Self-attention for Visual Recognition (TPAMI 2023) and UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning (ICLR 2022).

  • Thanks @xin-li-67 for helping with the integration of Uniformer!
  • This is the task of our OpenMMLabCamp, if you also wish to contribute code to us, feel free to refer to this link to pick up the task!

New Datasets

We have added support for two new datasets:

(CVPR 2023) UBody

UBody can boost 2D whole-body pose estimation and controllable image generation, especially for in-the-wild hand keypoint detection.

grouned_sam_osx_demo

  • Supported by @xiexinch

300W-LP

300W-LP contains the synthesized large-pose face images from 300W.

300wlp

  • Thanks @Yang-Changhui for helping with the integration of 300W-LP!
  • This is the task of our OpenMMLabCamp, if you also wish to contribute code to us, feel free to refer to this link to pick up the task!

Contributors

  • @Tau-J
  • @Ben-Louis
  • @xin-li-67
  • @Indigo6
  • @xiexinch
  • @tpoisonooo
  • @crazysteeaam
  • @yzd-v
  • @chaodyna
  • @lwttttt
  • @k-yomo
  • @LiuYi-Up
  • @ZhaoQiiii
  • @Yang-Changhui
  • @juxuan27

- Python
Published by Tau-J over 2 years ago

mmpose - MMPose v1.1.0 Release Note

New Datasets

We are glad to support 3 new datasets: - (CVPR 2023) Human-Art - (CVPR 2022) Animal Kingdom - (AAAI 2020) LaPa

(CVPR 2023) Human-Art

Human-Art is a large-scale dataset that targets multi-scenario human-centric tasks to bridge the gap between natural and artificial scenes.

image

Contents of Human-Art: - 50,000 images including human figures in 20 scenarios (5 natural scenarios, 3 2D artificial scenarios, and 12 2D artificial scenarios) - Human-centric annotations include human bounding box, 21 2D human keypoints, human self-contact keypoints, and description text - baseline human detector and human pose estimator trained on the joint of MSCOCO and Human-Art

Models trained on Human-Art: - HRNet - ViTPose - RTMPose

Thanks @juxuan27 for helping with the integration of Human-Art!

(CVPR 2022) Animal Kingdom

Animal Kingdom provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors.

image

Results comparison: <!--StartFragment-->

Arch | Input Size | PCK(0.05) Ours | Official Repo | Paper -- | -- | -- | -- | -- P1hrnetw32 | 256x256 | 0.6323 | 0.6342 | 0.6606 P2hrnetw32 | 256x256 | 0.3741 | 0.3726 | 0.393 P3mammalshrnetw32 | 256x256 | 0.571 | 0.5719 | 0.6159 P3amphibianshrnetw32 | 256x256 | 0.5358 | 0.5432 | 0.5674 P3reptileshrnetw32 | 256x256 | 0.51 | 0.5 | 0.5606 P3birdshrnetw32 | 256x256 | 0.7671 | 0.7636 | 0.7735 P3fisheshrnet_w32 | 256x256 | 0.6406 | 0.636 | 0.6825

<!--EndFragment-->

For more details, see this page

Thanks @Dominic23331 for helping with the integration of Animal Kingdom!

(AAAI 2020) LaPa

Landmark guided face Parsing dataset (LaPa) consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with an 11-category pixel-level label map and 106-point landmarks.

image

Supported by @Tau-J

New Config Type

MMEngine introduced the pure Python style configuration file: - Support navigating to base configuration file in IDE - Support navigating to base variable in IDE - Support navigating to source code of class in IDE - Support inheriting two configuration files containing the same field - Load the configuration file without other third-party requirements

Refer to the tutorial for more detailed usages.

image

We provided some examples here. Also, new config type of YOLOX-Pose is supported here. Feel free to try this new feature and give us your feedback!

Improved RTMPose

We combined public datasets and released more powerful RTMPose models: - 17-kpt and 26-kpt body models - 21-kpt hand models - 106-kpt face models

List of examples to deploy RTMPose: - RTMPose-Deploy @HW140701 @Dominic23331 - RTMPose-Deploy is a C++ code example for RTMPose localized deployment. - RTMPose inference with ONNXRuntime (Python) @IRONICBo - This example shows how to run RTMPose inference with ONNXRuntime in Python. - PoseTracker Android Demo - PoseTracker Android Demo Prototype based on mmdeploy.

Check out this page to know more.

Supported by @Tau-J

3D Pose Lifter Refactory

We have migrated SimpleBaseline3D and VideoPose3D into MMPose v1.1.0. Users can easily use Inferencer and body3d demo to conduct inference.

Below is an example of how to use Inferencer to predict 3d pose:

shell python demo/inferencer_demo.py tests/data/coco/000000000785.jpg \ --pose3d human3d --vis-out-dir vis_results/human3d \ --rebase-keypoint-height

image

Video result:

img_v2_45ba54f3-adae-49c7-bf45-07e84d49d21g

Supported by @LareinaM

Inference Speed-up & Webcam Inference

We have made a lot of improvements to our demo scripts: - Much higher inference speed - OpenCV-backend visualizer - All demos support inference with webcam

Take topdown_demo_with_mmdet.py as example, you can conduct inference with webcam by specifying --input webcam:

```shell

inference with webcam

python demo/topdowndemowithmmdet.py \ projects/rtmpose/rtmdet/person/rtmdetnano320-8xb32coco-person.py \ https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdetnano8xb32-100ecoco-obj365-person-05d8511e.pth \ projects/rtmpose/rtmpose/body2dkeypoint/rtmpose-m8xb256-420ecoco-256x192.py \ https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-msimcc-aic-cocopt-aic-coco420e-256x192-63eb25f7_20230126.pth \ --input webcam \ --show ```

Supported by @Ben-Louis and @LareinaM

New Contributors

  • @xin-li-67 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2205
  • @irexyc made their first contribution in https://github.com/open-mmlab/mmpose/pull/2216
  • @lu-minous made their first contribution in https://github.com/open-mmlab/mmpose/pull/2225
  • @FishBigOcean made their first contribution in https://github.com/open-mmlab/mmpose/pull/2286
  • @ATang0729 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2201
  • @HW140701 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2316
  • @IRONICBo made their first contribution in https://github.com/open-mmlab/mmpose/pull/2323
  • @shuheilocale made their first contribution in https://github.com/open-mmlab/mmpose/pull/2340
  • @Dominic23331 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2139
  • @notplus made their first contribution in https://github.com/open-mmlab/mmpose/pull/2365
  • @juxuan27 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2304
  • @610265158 made their first contribution in https://github.com/open-mmlab/mmpose/pull/2366
  • @CescMessi made their first contribution in https://github.com/open-mmlab/mmpose/pull/2385
  • @huangjiyi made their first contribution in https://github.com/open-mmlab/mmpose/pull/2467
  • @Billccx made their first contribution in https://github.com/open-mmlab/mmpose/pull/2417
  • @mareksubocz made their first contribution in https://github.com/open-mmlab/mmpose/pull/2474

Full Changelog: https://github.com/open-mmlab/mmpose/compare/v1.0.0...v1.1.0

- Python
Published by Tau-J almost 3 years ago

mmpose - MMPose V1.0.0 Release

Highlights

We are excited to announce the release of MMPose 1.0.0 as a part of the OpenMMLab 2.0 project! MMPose 1.0.0 introduces an updated framework structure for the core package and a new section called "Projects". This section showcases a range of engaging and versatile applications built upon the MMPose foundation.

In this latest release, we have significantly refactored the core package's code to make it clearer, more comprehensible, and disentangled. This has resulted in improved performance for several existing algorithms, ensuring that they now outperform their previous versions. Additionally, we have incorporated some cutting-edge algorithms, such as SimCC and ViTPose, to further enhance the capabilities of MMPose and provide users with a more comprehensive and powerful toolkit. The new "Projects" section serves as an essential addition to MMPose, created to foster innovation and collaboration among users. This section offers the following attractive features: - Flexible code contribution: Unlike the core package, the "Projects" section allows for a more flexible environment for code contributions, enabling faster integration of state-of-the-art models and features. - Showcase of diverse applications: Explore a wide variety of projects built upon the MMPose foundation, such as deployment examples and combinations of pose estimation with other tasks. - Fostering creativity and collaboration: Encourages users to experiment, build upon the MMPose platform, and share their innovative applications and techniques, creating an active community of developers and researchers. Discover the possibilities within the "Projects" section and join the vibrant MMPose community in pushing the boundaries of pose estimation applications!

Exciting Features

RTMPose

RTMPose is a high-performance real-time multi-person pose estimation framework designed for practical applications. RTMPose offers high efficiency and accuracy, with various models achieving impressive AP scores on COCO and fast inference speeds on both CPU and GPU. It is also designed for easy deployment across various platforms and backends, such as ONNX, TensorRT, ncnn, OpenVINO, Linux, Windows, NVIDIA Jetson, and ARM. Additionally, it provides a pipeline inference API and SDK for Python, C++, C#, Java, and other languages. [Project][Model Zoo][Tech Report]

image

  • Supported by @Tau-J in https://github.com/open-mmlab/mmpose/pull/1971

Inferencer

In this release, we introduce the MMPoseInferencer, a versatile API for inference that accommodates multiple input types. The API enables users to easily specify and customize pose estimation models, streamlining the process of performing pose estimation with MMPose.

Usage:

Python3 python demo/inferencer_demo.py ${INPUTS} --pose2d ${MODEL} [OPTIONS]

  • The INPUTS can be an image or video path, an image folder, or a webcam feed. You no longer need different demo scripts for various input types.
  • The inferencer supports specifying models with aliases such as 'human', 'animal', and 'face'. These aliases correspond to the advanced RTMPose models, which are fast and accurate. If you're unsure which model to use, the default one should suffice.

Example:

Python3 python demo/inferencer_demo.py tests/data/crowdpose --pose2d wholebody

All images located in the tests/data/crowdpose folder will be processed using RTMPose. Here are the visualization results:

Image 1 Image 2

For more details about Inferencer, please refer to https://mmpose.readthedocs.io/en/latest/user_guides/inference.html

  • Supported by @Ben-Louis in https://github.com/open-mmlab/mmpose/pull/1969

Visualization Improvements

In MMPose 1.0.0, we have enhanced the visualization capabilities for a more intuitive and insightful user experience, enabling a deeper understanding of the model's performance and keypoint predictions, and streamlining the process of fine-tuning and optimizing pose estimation models. The new visualization tool facilitates: 1. Heatmap Visualization: Supporting both 1D and 2D heatmaps, users can easily visualize the distribution of keypoints and their confidence levels, providing a clearer understanding of the model's keypoint predictions.

|2D Heatmap (ViTPose) | 1D Heatmap (RTMPose) | | -- | -- | | image |  image |

  1. Inference Result Visualization during Training and Testing: Users now can visualize pose estimation results in validation or testing phases, quickly identify and address potential issues for faster model iteration and improved performance. For guidance on setting up visualization during training and testing, please refer to train and test.
  • Support SimCC visualization by @jack0rich in https://github.com/open-mmlab/mmpose/pull/1912
  • Support OpenPose-style visualization by @Zheng-LinXiao in https://github.com/open-mmlab/mmpose/pull/2115

MMPose for AIGC

We are excited to introduce the MMPose4AIGC project, a powerful tool that allows users to extract human pose information using MMPose and seamlessly integrate it with the T2I Adapter demo to generate stunning AI-generated images. The project makes it easy for users to generate both OpenPose-style and MMPose-style skeleton images, which can then be used as inputs in the T2I Adapter demo to create captivating AI-generated content based on pose information. Discover the potential of pose-guided image generation with the MMPose4AIGC project and elevate your AI-generated content to new heights!

image

YOLOX-Pose

YOLOX-Pose is a YOLO-based human detector and pose estimator, leveraging the methodology described in YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss (CVPRW 2022). With its lightweight and fast performance, this model is ideally suited for handling crowded scenes. [Project][Paper]

image

  • Supported by @Ben-Louis in https://github.com/open-mmlab/mmpose/pull/2020

Optimizations

In addition to new features, MMPose 1.0.0 delivers key optimizations for an enhanced user experience. With PyTorch 2.0 compatibility and a streamlined Codec module, you'll enjoy a more efficient and user-friendly pose estimation workflow like never before.

PyTorch 2.0 Compatibility

MMPose 1.0.0 is now compatible with PyTorch 2.0, ensuring that users can leverage the latest features and performance improvements offered by the PyTorch 2.0 framework when using MMPose. With the integration of inductor, users can expect faster model speeds. The table below shows several example models:

| Model | Training Speed | Memory | | :-- | :--: | :--: | | ViTPose-B | 29.6% ↑ (0.931 → 0.655) | 10586 → 10663 | | ViTPose-S | 33.7% ↑ (0.563 → 0.373) | 6091 → 6170 | | HRNet-w32 | 12.8% ↑ (0.553 → 0.482) | 9849 → 10145 | | HRNet-w48 | 37.1% ↑ (0.437 → 0.275) | 7319 → 7394 | | RTMPose-t | 6.3% ↑ (1.533 → 1.437) | 6292 → 6489 | | RTMPose-s | 13.1% ↑ (1.645 → 1.430) | 9013 → 9208 |

  • Pytorch 2.0 test, add projects doc and refactor by @LareinaM in https://github.com/open-mmlab/mmpose/pull/2136

New Design: Codecs

In pose estimation tasks, various algorithms require different target formats, such as normalized coordinates, vectors, and heatmaps. MMPose 1.0.0 introduces a unified Codec module to streamline the encoding and decoding processes:

image

  • Encoder: Transforms input image space coordinates into required target formats.
  • Decoder: Transforms model outputs into input image space coordinates, performing the inverse operation of the encoder. This integration offers a more coherent and user-friendly experience when working with different pose estimation algorithms. For a detailed introduction to codecs, including concrete examples, please refer to our guide on learn about Codecs

Bug Fixes

  • [Fix] fix readthedocs compiling requirements by @ly015 in https://github.com/open-mmlab/mmpose/pull/2071
  • [Fix] fix online documentation by @ly015 in https://github.com/open-mmlab/mmpose/pull/2073
  • [Fix] fix online docs by @ly015 in https://github.com/open-mmlab/mmpose/pull/2075
  • [Fix] fix warnings when falling back to mmengine registry by @ly015 in https://github.com/open-mmlab/mmpose/pull/2082
  • [Fix] fix CI by @ly015 in https://github.com/open-mmlab/mmpose/pull/2088
  • [Fix] fix model names in metafiles by @Ben-Louis in https://github.com/open-mmlab/mmpose/pull/2093
  • [Fix] fix simcc visualization by @Tau-J in https://github.com/open-mmlab/mmpose/pull/2130

New Contributors

  • @ChenZhenGui made their first contribution in https://github.com/open-mmlab/mmpose/pull/1800
  • @xinxinxinxu made their first contribution in https://github.com/open-mmlab/mmpose/pull/1843
  • @jack0rich made their first contribution in https://github.com/open-mmlab/mmpose/pull/1912
  • @zwfcrazy made their first contribution in https://github.com/open-mmlab/mmpose/pull/1944
  • @LKJacky made their first contribution in https://github.com/open-mmlab/mmpose/pull/2024
  • @tongda made their first contribution in https://github.com/open-mmlab/mmpose/pull/2028
  • @LRuid made their first contribution in https://github.com/open-mmlab/mmpose/pull/2055

Full Changelog: https://github.com/open-mmlab/mmpose/compare/v0.29.0...v1.0.0

- Python
Published by Tau-J about 3 years ago

mmpose - MMPose V1.0.0rc1 Release

Highlights

  • Release RTMPose, a high-performance real-time pose estimation algorithm with cross-platform deployment and inference support. See details at the project page
  • Support several new algorithms: ViTPose (arXiv'2022), CID (CVPR'2022), DEKR (CVPR'2021)
  • Add Inferencer, a convenient inference interface that perform pose estimation and visualization on images, videos and webcam streams with only one line of code
  • Introduce Project, a new form for rapid and easy implementation of new algorithms and features in MMPose, which is more handy for community contributors

New Features

Improvements

  • Improve documentation quality (#1846, #1858, #1872, #1899, #1925, #1945, #1952, #1990, #2023, #2042)
  • Support visualizing keypoint indices (#2051)
  • Support OpenPose style visualization (#2055)
  • Accelerate image transpose in data pipelines with tensor operation (#1976)
  • Support auto-import modules from registry (#1961)
  • Support keypoint partition metric (#1944)
  • Support SimCC 1D-heatmap visualization (#1912)
  • Support saving predictions and data metainfo in demos (#1814, #1879)
  • Support SimCC with DARK (#1870)
  • Remove Gaussian blur for offset maps in UDP-regress (#1815)
  • Refactor encoding interface of Codec for better extendibility and easier configuration (#1781)
  • Support evaluating CocoMetric without annotation file (#1722)
  • Improve unit tests (#1765)

Bug Fixes

  • Fix repeated warnings from different ranks (#2053)
  • Avoid frequent scope switching when using mmdet inference api (#2039)
  • Remove EMA parameters and message hub data when publishing model checkpoints (#2036)
  • Fix metainfo copying in dataset class (#2017)
  • Fix top-down demo bug when there is no object detected (#2007)
  • Fix config errors (#1882, #1906, #1995)
  • Fix image demo failure when GUI is unavailable (#1968)
  • Fix bug in AdaptiveWingLoss (#1953)
  • Fix incorrect importing of RepeatDataset which is deprecated (#1943)
  • Fix bug in bottom-up datasets that ignores images without instances (#1752, #1936)
  • Fix upstream dependency issues (#1867, #1921)
  • Fix evaluation issues and update results (#1763, #1773, #1780, #1850, #1868)
  • Fix local registry missing warnings (#1849)
  • Remove deprecated scripts for model deployment (#1845)
  • Fix a bug in input transformation in BaseHead (#1843)
  • Fix an interface mismatch with MMDetection in webcam demo (#1813)
  • Fix a bug in heatmap visualization that causes incorrect scale (#1800)
  • Add model metafiles (#1768)

New Contributors

  • @ChenZhenGui made their first contribution in https://github.com/open-mmlab/mmpose/pull/1800
  • @LareinaM made their first contribution in https://github.com/open-mmlab/mmpose/pull/1845
  • @xinxinxinxu made their first contribution in https://github.com/open-mmlab/mmpose/pull/1843
  • @jack0rich made their first contribution in https://github.com/open-mmlab/mmpose/pull/1912
  • @zwfcrazy made their first contribution in https://github.com/open-mmlab/mmpose/pull/1944
  • @LKJacky made their first contribution in https://github.com/open-mmlab/mmpose/pull/2024
  • @tongda made their first contribution in https://github.com/open-mmlab/mmpose/pull/2028
  • @LRuid made their first contribution in https://github.com/open-mmlab/mmpose/pull/2055
  • @Zheng-LinXiao made their first contribution in https://github.com/open-mmlab/mmpose/pull/2057

Full Changelog: https://github.com/open-mmlab/mmpose/compare/v1.0.0rc0...v1.0.0rc1

- Python
Published by ly015 about 3 years ago

mmpose - MMPose V0.29.0 Release

Highlights

  • Support DEKR, CVPR'2021 (#1693) @JinluZhang1126, @jin-s13, @Ben-Louis
  • Support CID, CVPR'2022 (#1604) @kennethwdk, @jin-s13, @Ben-Louis

Improvements

  • Improve 3D pose estimation demo with viewpoint & height controlling and enriched input formats (#1481, #1490) @pallgeuer, @liqikai9
  • Improve smoothing of 3D video pose estimation (#1479) @darcula1993
  • Improve dependency management for smoother installation (#1491) @danielbraun89
  • Improve documentation quality (#1680, #1525, #1516, #1513, #1511, #1507, #1506, #1525) @Ben-Louis, @Yulv-git, @jin-s13

Bug Fixes

  • Update dependency versions of pre-commit-hooks (#1706, #1678) @Ben-Louis, @liqikai9
  • Fix missing data transforms in RLE configs (#1632) @Ben-Louis
  • Fix a bug in fliplr_joints that causes error when keypoint visibility has float values (#1589) @walsvid
  • Fix hsigmoid default parameters (#1575) @Ben-Louis
  • Fix a bug in UDP decoding (#1565) @liqikai9
  • Fix a bug in pose tracking demo with non-COCO dataset (#1504) @daixinghome
  • Fix typos and unused contents in configs (#1499, #1496) @liqikai9

- Python
Published by ly015 over 3 years ago

mmpose - MMPose V1.0.0rc0 Release

New Features

  • Support 4 light-weight pose estimation algorithms: SimCC (ECCV'2022), Debias-IPR (ICCV'2021), IPR (ECCV'2018), and DSNT (ArXiv'2018) (#1628) @Tau-J

Migrations

  • Add Webcam API in MMPose 1.0 (#1638, #1662) @Ben-Louis
  • Add codec for Associative Embedding (beta) (#1603) @ly015

Improvements

  • Add a colab tutorial for MMPose 1.0 (#1660) @Tau-J
  • Add model index in config folder (#1710, #1709, #1627) @ly015, @Tau-J, @Ben-Louis
  • Update and improve documentation (#1692, #1656, #1681, #1677, #1664, #1659) @Tau-J, @Ben-Louis, @liqikai9
  • Improve config structures and formats (#1651) @liqikai9

Bug Fixes

  • Update mmengine version requirements (#1715) @Ben-Louis
  • Update dependencies of pre-commit hooks (#1705) @Ben-Louis
  • Fix mmcv version in DockerFile (#1704)
  • Fix a bug in setting dataset metainfo in configs (#1684) @ly015
  • Fix a bug in UDP training (#1682) @liqikai9
  • Fix a bug in Dark decoding (#1676) @liqikai9
  • Fix bugs in visualization (#1671, #1668, #1657) @liqikai9, @Ben-Louis
  • Fix incorrect flops calculation (#1669) @liqikai9
  • Fix tensor.tile compatibility issue for pytorch 1.6 (#1658) @ly015
  • Fix compatibility with MultilevelPixelData (#1647) @liqikai9

- Python
Published by ly015 over 3 years ago

mmpose - MMPose V1.0.0-beta Release

We are excited to announce the release of MMPose 1.0.0beta. MMPose 1.0.0beta is the first version of MMPose 1.x, a part of the OpenMMLab 2.0 projects. Built upon the new training engine.

Highlights

  • New engines. MMPose 1.x is based on MMEngine, which provides a general and powerful runner that allows more flexible customizations and significantly simplifies the entrypoints of high-level interfaces.

  • Unified interfaces. As a part of the OpenMMLab 2.0 projects, MMPose 1.x unifies and refactors the interfaces and internal logics of train, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logics to allow the emergence of multi-task/modality algorithms.

  • More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.

Breaking Changes

In this release, we made lots of major refactoring and modifications. Please refer to the migration guide for details and migration instructions.

- Python
Published by ly015 almost 4 years ago

mmpose - MMPose V0.28.1 Release

This release is meant to fix the compatibility with the latest mmcv v1.6.1

- Python
Published by ly015 almost 4 years ago

mmpose - MMPose V0.28.0 Release

Highlights

  • Support TCFormer backbone, CVPR'2022 (#1447, #1452) @zengwang430521
  • Add RLE models on COCO dataset (#1424) @Indigo6, @Ben-Louis, @ly015
  • Update swin models with better performance (#1467) @jin-s13

New Features

  • Support TCFormer backbone, CVPR'2022 (#1447, #1452) @zengwang430521
  • Add RLE models on COCO dataset (#1424) @Indigo6, @Ben-Louis, @ly015
  • Support layer decay optimizer conctructor and learning rate decay optimizer constructor (#1423) @jin-s13

Improvements

  • Improve documentation quality (#1416, #1421, #1423, #1426, #1458, #1463) @ly015, @liqikai9
  • Support installation by mim (#1425) @liqikai9
  • Support PAVI logger (#1434) @EvelynWang-0423
  • Add progress bar for some demos (#1454) @liqikai9
  • Webcam API supports quick device setting in terminal commands (#1466) @ly015
  • Update swin models with better performance (#1467) @jin-s13

Bug Fixes

  • Rename custom_hooks_config to custom_hooks in configs to align with the documentation (#1427) @ly015
  • Fix deadlock issue in Webcam API (#1430) @ly015
  • Fix smoother configs in video 3D demo (#1457) @ly015

- Python
Published by ly015 almost 4 years ago

mmpose - MMPose V0.27.0 Release

Highlights

New Features

  • Support gesture recognition algorithm MTUT CVPR'2019 and dataset NVGesture CVPR'2016 (#1380) @Ben-Louis

Improvements

  • Upgrade Webcam API and related documents (#1393, #1404, #1413) @ly015
  • Support exporting COCO inference result without the annotation file (#1368) @liqikai9
  • Replace markdownlint with mdformat in CI to avoid the dependence on ruby #1382 @ly015
  • Improve documentation quality (#1385, #1394, #1395, #1408) @chubei-oppen, @ly015, @liqikai9

Bug Fixes

  • Fix xywh->xyxy bbox conversion in dataset sanity check (#1367) @jin-s13
  • Fix a bug in two-stage 3D keypoint demo (#1373) @ly015
  • Fix out-dated settings in PVT configs (#1376) @ly015
  • Fix myst settings for document compiling (#1381) @ly015
  • Fix a bug in bbox transform (#1384) @ly015
  • Fix inaccurate description of min_keypoints in tracking apis (#1398) @pallgeuer
  • Fix warning with torch.meshgrid (#1402) @pallgeuer
  • Remove redundant transformer modules from mmpose.datasets.backbones.utils (#1405) @ly015

- Python
Published by ly015 almost 4 years ago

mmpose - MMPose V0.26.0 Release

Highlights

New Features

Improvements

  • Speed up inference and reduce CPU usage by optimizing the pre-processing pipeline (#1320) @chenxinfeng4, @liqikai9
  • Video demo supports models that requires multi-frame inputs (#1300) @liqikai9, @jin-s13
  • Update benchmark regression list (#1328) @ly015, @liqikai9
  • Remove unnecessary warnings in TopDownPoseTrack18VideoDataset (#1335) @liqikai9
  • Improve documentation quality (#1313, #1305) @Ben-Louis, @ly015
  • Update deprecating settings in configs (#1317) @ly015

Bug Fixes

  • Fix a bug in human skeleton grouping that may skip the matching process unexpectedly when ignore_to_much is True (#1341) @daixinghome
  • Fix a GPG key error that leads to CI failure (#1354) @ly015
  • Fix bugs in distributed training script (#1338, #1298) @ly015
  • Fix an upstream bug in xtoccotools that causes incorrect AP(M) results (#1308) @jin-s13, @ly015
  • Fix indentiation errors in the colab tutorial (#1298) @YuanZi1501040205
  • Fix incompatible model weight initialization with other OpenMMLab codebases (#1329) @274869388
  • Fix HRNet FP16 checkpoints download URL (#1309) @YinAoXiong
  • Fix typos in body3d_two_stage_video_demo.py (#1295) @mucozcan

Breaking Changes

  • Refactor bbox processing in datasets and pipelines (#1311) @ly015, @Ben-Louis The bbox format conversion (xywh to center-scale) and random translation are moved from the dataset to the pipeline. The comparison between new and old version is as below:
v0.26.0 v0.25.0
Dataset
(e.g. TopDownCOCODataset) 		```python ... # Data sample only contains bbox rec.append({ 'bbox': obj['clean_bbox][:4], ... }) ``` ```python ... # Convert bbox from xywh to center-scale center, scale = self._xywh2cs(*obj['clean_bbox'][:4]) # Data sample contains center and scale rec.append({ 'bbox': obj['clean_bbox][:4], 'center': center, 'scale': scale, ... }) ```
Pipeline Config
(e.g. HRNet+COCO)		 ```python ... train_pipeline = [ dict(type='LoadImageFromFile'), # Convert bbox from xywh to center-scale dict(type='TopDownGetBboxCenterScale', padding=1.25), # Randomly shift bbox center dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3), ... ] ``` ```python ... train_pipeline = [ dict(type='LoadImageFromFile'), ... ] ```
Advantage
  • Simpler data sample content
  • Flexible bbox format conversion and augmentation
  • Apply bbox random translation every epoch (instead of only applying once at the annotation loading)
    		• -
    BC Breaking The method _xywh2cs of dataset base classes (e.g. Kpt2dSviewRgbImgTopDownDataset) will be deprecated in the future. Custom datasets will need modifications to move the bbox format conversion to pipelines. -

    - Python
    Published by ly015 about 4 years ago

    mmpose - MMPose V0.25.1 Release

    This release is meant to fix the compatibility with the latest mmcv v1.5.0

    - Python
    Published by ly015 about 4 years ago

    mmpose - MMPose V0.25.0 Release

    Highlights

    New Features

    Improvements

    • Update HRFormer configs and checkpoints with relative position bias (#1245) @zengwang430521
    • Support using different random seed for each distributed node (#1257, #1229) @ly015
    • Improve documentation quality (#1275, #1255, #1258, #1249, #1247, #1240, #1235) @ly015, @jin-s13, @YoniChechik

    Bug Fixes

    • Fix keypoint index in RHD dataset meta information (#1265) @liqikai9
    • Fix pre-commit hook unexpected behavior on Windows (#1282) @liqikai9
    • Remove python-dev installation in CI (#1276) @ly015
    • Unify hyphens in argument names in tools and demos (#1271) @ly015
    • Fix ambiguous channel size in channel_shuffle that may cause exporting failure (#1242) @PINTO0309
    • Fix a bug in Webcam API that causes single-class detectors fail (#1239) @674106399
    • Fix the issue that custom_hook can not be set in configs (#1236) @bladrome
    • Fix incompatible MMCV version in DockerFile (#raykindle)
    • Skip invisible joints in visualization (#1228) @womeier

    - Python
    Published by ly015 about 4 years ago

    mmpose - MMPose V0.24.0 Release

    Highlights

    New Features

    Improvements

    • Refactor multi-view 3D pose estimation framework towards better modularization and expansibility (#1196) @wusize
    • Add WebcamAPI documents and tutorials (#1187) @ly015
    • Refactor dataset evaluation interface to align with other OpenMMLab codebases (#1209) @ly015
    • Add deprecation message for deploy tools since MMDeploy has supported MMPose (#1207) @QwQ2000
    • Improve documentation quality (#1206, #1161) @ly015
    • Switch to OpenMMLab official pre-commit-hook for copyright check (#1214) @ly015

    Bug Fixes

    • Fix hard-coded data collating and scattering in inference (#1175) @ly015
    • Fix model configs on JHMDB dataset (#1188) @jin-s13
    • Fix area calculation in pose tracking inference (#1197) @pallgeuer
    • Fix registry scope conflict of module wrapper (#1204) @ly015
    • Update MMCV installation in CI and documents (#1205)
    • Fix incorrect color channel order in visualization functions (#1212) @ly015

    - Python
    Published by ly015 about 4 years ago

    mmpose - MMPose V0.23.0 Release

    Highlights

    New Features

    • Add MMPose Webcam API: A simple yet powerful tools to develop interactive webcam applications with MMPose functions. (#1178, #1173, #1173, #1143, #1094, #1133, #1098, #1160) @ly015, @jin-s13, @liqikai9, @wusize, @luminxu, @zengwang430521 @mzr1996
    • Support ConcatDataset (#1139) @Canwang-sjtu
    • Support CPU training and testing (#1157) @ly015

    Improvements

    • Add multi-processing configurations to speed up distributed training and testing (#1146) @ly015

    • Add default runtime config (#1145)

    • Upgrade isort in pre-commit hook (#1179) @liqikai9

    • Update README and documents (#1171, #1167, #1153, #1149, #1148, #1147, #1140) @jin-s13, @wusize, @TommyZihao, @ly015

    Bug Fixes

    • Fix undeterministic behavior in pre-commit hooks (#1136) @jin-s13
    • Deprecate the support for "python setup.py test" (#1179) @ly015
    • Fix incompatible settings with MMCV on HSigmoid default parameters (#1132) @ly015
    • Fix albumentation installation (#1184) @BIGWangYuDong

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.22.0 Release

    Highlights

    New Features

    Improvements

    Bug Fixes

    • Fix a bug in Dark UDP postprocessing that causes error when the channel number is large. (#1079, #1116) @X00123, @jin-s13
    • Fix hard-coded sigmas in bottom-up image demo (#1107, #1101) @chenxinfeng4, @liqikai9
    • Fix unstable checks in unit tests (#1112) @ly015
    • Do not destroy NULL windows if args.show==False in demo scripts (#1104) @bladrome

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.21.0 Release

    Highlights

    New Features

    Improvements

    Bug Fixes

    • Update pose tracking demo to be compatible with latest mmtracking (#1014) @jin-s13
    • Fix symlink creation failure when installed in Windows environments (#1039) @QwQ2000
    • Fix AP-10K dataset sigmas (#1040) @jin-s13

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.20.0 Release

    Highlights

    • Add AP-10K dataset for animal pose estimation (#987) @Annbless, @AlexTheBad, @jin-s13, @ly015
    • Support TorchServe (#979) @ly015

    New Features

    • Add AP-10K dataset for animal pose estimation (#987) @Annbless, @AlexTheBad, @jin-s13, @ly015
    • Add HRNetv2 checkpoints on 300W and COFW datasets (#980) @jin-s13
    • Support TorchServe (#979) @ly015

    Bug Fixes

    • Fix some deprecated or risky settings in configs (#963, #976, #992) @jin-s13, @wusize
    • Fix issues of default arguments of training and testing scripts (#970, #985) @liqikai9, @wusize
    • Fix heatmap and tag size mismatch in bottom-up with UDP (#994) @wusize
    • Fix python3.9 installation in CI (#983) @ly015
    • Fix model zoo document integrity issue (#990) @jin-s13

    Improvements

    • Support non-square input shape for bottom-up (#991) @wusize
    • Add image and video resources for demo (#971) @liqikai9
    • Use CUDA docker images to accelerate CI (#973) @ly015
    • Add codespell hook and fix detected typos (#977) @ly015

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.19.0 Release

    Highlights

    • Add models for Associative Embedding with Hourglass network backbone (#906, #955) @jin-s13, @luminxu
    • Support COCO-Wholebody-Face and COCO-Wholebody-Hand datasets (#813) @jin-s13, @innerlee, @luminxu
    • Upgrade dataset interface (#901, #924) @jin-s13, @innerlee, @ly015, @liqikai9
    • New style of documentation (#945) @ly015

    New Features

    • Add models for Associative Embedding with Hourglass network backbone (#906, #955) @jin-s13, @luminxu
    • Support COCO-Wholebody-Face and COCO-Wholebody-Hand datasets (#813) @jin-s13, @innerlee, @luminxu
    • Add pseudo-labeling tool to generate COCO style keypoint annotations with given bounding boxes (#928) @soltkreig
    • New style of documentation (#945) @ly015

    Bug Fixes

    • Fix segmentation parsing in Macaque dataset preprocessing (#948) @jin-s13
    • Fix dependencies that may lead to CI failure in downstream projects (#936, #953) @RangiLyu, @ly015
    • Fix keypoint order in Human3.6M dataset (#940) @ttxskk
    • Fix unstable image loading for Interhand2.6M (#913) @zengwang430521

    Improvements

    • Upgrade dataset interface (#901, #924) @jin-s13, @innerlee, @ly015, @liqikai9
    • Improve demo usability and stability (#908, #934) @ly015
    • Standardize model metafile format (#941) @ly015
    • Support persistent_worker and several other arguments in configs (#946) @jin-s13
    • Use MMCV root model registry to enable cross-project module building (#935) @RangiLyu
    • Improve the document quality (#916, #909, #942, #913, #956) @jin-s13, @ly015, @bit-scientist, @zengwang430521
    • Improve pull request template (#952, #954) @ly015

    Breaking Changes

    • Upgrade dataset interface (#901) @jin-s13, @innerlee, @ly015

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.18.0 Release

    Improvements

    • Add automatic benchmark regression tools (#849, #880, #885) @liqikai9, @ly015
    • Add copyright information and checking hook (#872)
    • Add PR template (#875) @ly015
    • Add citation information (#876) @ly015
    • Add python3.9 in CI (#877, #883) @ly015
    • Improve the quality of the documents (#845, #845, #848, #867, #870, #873, #896) @jin-s13, @ly015, @zhiqwang

    Bug Fixes

    • Fix redundant model weight loading in pytorch-to-onnx conversion (#850) @ly015
    • Fix a bug in updatemodelindex.py that may cause pre-commit hook failure(#866) @ly015
    • Fix a bug in interhand3dhead (#890) @zengwang430521
    • Fix pose tracking demo failure caused by out-of-date configs (#891)

    - Python
    Published by ly015 over 4 years ago

    mmpose - MMPose V0.17.0 Release

    Highlights

    1. Support "Lite-HRNet: A Lightweight High-Resolution Network" CVPR'2021 (#733,#800) @jin-s13
    2. Add 3d body mesh demo (#771) @zengwang430521
    3. Add Chinese documentation (#787, #798, #799, #802, #804, #805, #815, #816, #817, #819, #839) @ly015, @luminxu, @jin-s13, @liqikai9, @zengwang430521
    4. Add Colab Tutorial (#834) @ly015

    New Features

    Bug Fixes

    • Fix mpii pckh@0.1 index (#773) @jin-s13
    • Fix multi-node distributed test (#818) @ly015
    • Fix docstring and init_weights error of ShuffleNetV1 (#814) @Junjun2016
    • Fix imshow_bbox error when input bboxes is empty (#796) @ly015
    • Fix model zoo doc generation (#778) @ly015
    • Fix typo (#767), (#780, #782) @ly015, @jin-s13

    Breaking Changes

    • Use MMCV EvalHook (#686) @ly015

    Improvements

    • Add pytest.ini and fix docstring (#812) @jin-s13
    • Support None as target weights in losses (#829) @Ezra-Yu
    • Move processmmdetresults into inference.py (#831) @ly015
    • Update resource limit (#783) @jin-s13
    • Use COCO 2D pose model in 3D demo examples (#785) @ly015
    • Change model zoo titles in the doc from center-aligned to left-aligned (#792, #797) @ly015
    • Support MIM (#706, #794) @ly015
    • Update out-of-date configs (#827) @jin-s13
    • Remove opencv-python-headless dependency by albumentations (#833) @ly015
    • Update QQ QR code in README_CN.md (#832) @ly015

    - Python
    Published by ly015 almost 5 years ago

    mmpose - MMPose V0.16.0 Release

    Demo Video

    Highlights

    1. Support "ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search" CVPR'2021 (#742,#755).
    2. Support MPI-INF-3DHP dataset (#683,#746,#751).
    3. Add webcam demo tool (#729)
    4. Add 3d body and hand pose estimation demo (#704, #727).

    New Features

    Bug Fixes

    Breaking Changes

    • Switch to MMCV MODEL_REGISTRY (#669)

    Improvements

    • Refactor MeshMixDataset (#752)
    • Rename 'GaussianHeatMap' to 'GaussianHeatmap' (#745)
    • Update out-of-date configs (#734)
    • Improve compatibility for breaking changes (#731)
    • Enable to control radius and thickness in visualization (#722)
    • Add regex dependency (#720)

    - Python
    Published by ly015 almost 5 years ago

    mmpose - MMPose V0.15.0 Release

    Highlights

    1. Support 3d video pose estimation (VideoPose3D).
    2. Support 3d hand pose estimation (InterNet).
    3. Improve presentation of the model zoo.

    New Features

    • Support "InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image" (ECCV‘20) (#624)
    • Support "3D human pose estimation in video with temporal convolutions and semi-supervised training" (CVPR'19) (#602,#681)
    • Support 3d pose estimation demo (#653,#670)
    • Support bottom-up whole-body pose estimation (#689)
    • Support mmcli (#634)

    Bug Fixes

    Breaking Changes

    • Reorganize configs by tasks, algorithms, datasets, and techniques (#647)
    • Rename heads and detectors (#667)

    Improvements

    • Add radius and thickness parameters in visualization (#638)
    • Add trans_prob parameter in TopDownRandomTranslation (#650)
    • Switch to MMCV MODEL_REGISTRY (#669)
    • Update dependencies (#674,#676)

    - Python
    Published by ly015 almost 5 years ago

    mmpose - MMPose V0.14.0 Release

    Highlights

    1. Support animal pose estimation with 7 popular datasets.
    2. Support "A simple yet effective baseline for 3d human pose estimation" (ICCV'17).

    New Features

    • Support "A simple yet effective baseline for 3d human pose estimation" (ICCV'17) (#554,#558,#566,#570,#589)
    • Support animal pose estimation (#559,#561,#563,#571,#603,#605)
    • Support Horse-10 dataset (#561), MacaquePose dataset (#561), Vinegar Fly dataset (#561), Desert Locust dataset (#561), Grevy's Zebra dataset (#561), ATRW dataset (#571), and Animal-Pose dataset (#603).
    • Support bottom-up pose tracking demo (#574).
    • Support FP16 training (#584,#616,#626).
    • Support NMS for bottom-up (#609).

    Bug Fixes

    • Fix bugs in the top-down demo, when there are no people in the images (#569).
    • Fix the links in the doc (#612).

    Improvements

    - Python
    Published by ly015 about 5 years ago

    mmpose - MMPose V0.13.0 Release

    Highlights

    1. Support Wingloss.
    2. Support RHD hand dataset.

    New Features

    • Support Wingloss (#482)
    • Support RHD hand dataset (#523, #551)
    • Support Human3.6m dataset for 3d keypoint detection (#518, #527)
    • Support TCN model for 3d keypoint detection (#521, #522)
    • Support Interhand3D model for 3d hand detection (#536)
    • Support Multi-task detector (#480)

    Bug Fixes

    • Fix PCKh@0.1 calculation (#516)
    • Fix unittest (#529)
    • Fix circular importing (#542)
    • Fix bugs in bottom-up keypoint score (#548)

    Improvements

    - Python
    Published by ly015 about 5 years ago

    mmpose - MMPose V0.12.0 Release

    Highlights

    1. Support DeepPose algorithm.

    New Features

    • Support DeepPose algorithm (#446, #461)
    • Support interhand3d dataset (#468)
    • Support Albumentation pipeline (#469)
    • Support PhotometricDistortion pipeline (#485)
    • Set seed option for training (#493)
    • Add demos for face keypoint detection (#502)

    Bug Fixes

    • Change channel order according to configs (#504)
    • Fix num_factors in UDP encoding (#495)
    • Fix configs (#456)

    Breaking Changes

    • Refactor configs for wholebody pose estimation (#487, #491)
    • Rename decode function for heads (#481)

    Improvements

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.11.0 Release

    Highlights

    1. Support fashion landmark detection.
    2. Support face keypoint detection.
    3. Support pose tracking with MMTracking.

    New Features

    • Support fashion landmark detection (DeepFashion) (#413)
    • Support face keypoint detection (300W, AFLW, COFW, WFLW) (#367)
    • Support pose tracking demo with MMTracking (#427)
    • Support face demo (#443)
    • Support AIC dataset for bottom-up methods (#438, #449)

    Bug Fixes

    • Fix multi-batch training (#434)
    • Fix sigmas in AIC dataset (#441)
    • Fix config file (#420)

    Breaking Changes

    • Refactor Heads (#382)

    Improvements

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.10.0 Release

    Highlights

    1. Support more human pose estimation methods.
    2. Support pose tracking.
    3. Support multi-batch inference.
    4. Add some useful tools, including analyze_logs, get_flops, print_config.
    5. Support more backbone networks.

    New Features

    • Support UDP (#353, #371, #402)
    • Support multi-batch inference (#390)
    • Support MHP dataset (#386)
    • Support pose tracking demo (#380)
    • Support MPII-TRB demo (#372)
    • Support MobileNet for hand pose estimation (#377)
    • Support ResNest backbone (#370)
    • Support VGG backbone (#370)
    • Add some useful tools, including analyze_logs, get_flops, print_config (#324)

    Bug Fixes

    • Fix bugs in pck evaluation (#328)
    • Fix model download links in README (#396, #397)
    • Fix CrowdPose annotations and update benchmarks (#384)
    • Fix modelzoo stat (#354, #360, #362)
    • Fix config files for AIC datasets (#340)

    Breaking Changes

    • Rename image_thr to det_bbox_thr for top-down methods.

    Improvements

    • Organize the readme files (#398, #399, #400)
    • Check linting for markdown (#379)
    • Add faq.md (#350)
    • Remove PyTorch 1.4 in CI (#338)
    • Add pypi badge in readme (#329)

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.9.0 Release

    Highlights

    1. Support more human pose estimation methods.
    2. Support video pose estimation datasets.
    3. Support Onnx model conversion.

    New Features

    • Support MSPN (#278)
    • Support RSN (#221, #318)
    • Support new post-processing method for MSPN & RSN (#288)
    • Support sub-JHMDB dataset (#292)
    • Support urls for pre-trained models in config files (#232)
    • Support Onnx (#305)

    Bug Fixes

    • Fix model download links in README (#255, #315)

    Breaking Changes

    • post_process=True|False and unbiased_decoding=True|False are deprecated, use post_process=None|default|unbiased etc. instead (#288)

    Improvements

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.8.0 Release

    Highlights

    1. Support more human pose estimation datasets.
    2. Support more 2D hand keypoint estimation datasets.
    3. Support adversarial training for 3D human shape recovery.
    4. Support multi-stage losses.
    5. Support mpii demo.

    New Features

    Bug Fixes

    • Fix config files (#190)

    Improvements

    • Add mpii demo (#216)
    • Improve README (#181, #183, #208)
    • Support return heatmaps and backbone features (#196, #212)
    • Support different return formats of mmdetection models (#217)

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.7.0 Release

    Highlights

    1. Support HMR for 3D human shape recovery.
    2. Support WholeBody human pose estimation.
    3. Support more 2D hand keypoint estimation datasets.
    4. Add more popular backbones & enrich the modelzoo.
      • ShuffleNetv2
    5. Support hand demo and whole-body demo.

    New Features

    Bug Fixes

    • Fix typos in docs (#121)
    • Fix assertion (#142)

    Improvements

    • Add tools to transform .mat format to .json format (#126)
    • Add hand demo (#115)
    • Add whole-body demo (#163)
    • Reuse mmcv utility function and update version files (#135, #137)
    • Enrich the modelzoo (#147, #169)
    • Improve docs (#174, #175, #178)
    • Improve README (#176)
    • Improve version.py (#173)

    - Python
    Published by innerlee over 5 years ago

    mmpose - MMPose V0.6.0 Release

    Highlights

    1. Add popular backbones & enrich the modelzoo.

      • ResNext
      • SEResNet
      • ResNetV1D
      • MobileNetv2
      • ShuffleNetv1
      • CPM (Convolutional Pose Machine)

    2. Add popular datasets:

    3. Support 2d hand keypoint estimation.

    4. Support bottom-up inference.

    New Features - Support OneHand10K dataset (#52). - Support MPII dataset (#55). - Support MPII-TRB dataset (#19, #47, #48). - Support OCHuman dataset (#70). - Support AIChallenger dataset (#87). - Support multiple backbones (#26). - Support CPM model (#56).

    Bug Fixes - Fix configs for MPII & MPII-TRB datasets (#93). - Fix the bug of missing test_pipeline in configs (#14). - Fix typos (#27, #28, #50, #53, #63).

    Improvements - Update benchmark (#93). - Add Dockerfile (#44). - Improve unittest coverage and minor fix (#18). - Support CPUs for train/val/demo (#34). - Support bottom-up demo (#69). - Add tools to publish model (#62). - Enrich the modelzoo (#64, #68, #82).

    - Python
    Published by innerlee over 5 years ago