Thank you. We assume that you have installed MMAction2 from source. Code Issues A OpenMMLAB toolbox for human pose estimation, skeleton-based action recognition, and action synthesis. @misc {goyal2017something, title = {The "something something" video database for learning and evaluating visual common sense}, author = {Raghav Goyal and Samira Ebrahimi Kahou and Vincent Michalski and Joanna Materzyńska and Susanne Westphal and Heuna Kim and Valentin Haenel and Ingo Fruend and Peter Yianilos and Moritz Mueller-Freitag and Florian Hoppe and Christian Thurau and Ingo Bax and Two essential components of TPN, the source of features and the fusion of features, form a feature hierarchy for the backbone so that it can capture action instances at various tempos. Allowed values depend on the dataset, e. g. Welcome to MMAction2’s documentation! Audio-based Action Recognition Models; Skeleton-based Action Recognition Models; Spatio Temporal Action Detection Models; You signed in with another tab or window. Actor-centric relation network. com A Human Action Recognition pipeline using MMAction2 and kinetics400 dataset. Testing. 9 top-1 accuracy on Kinetics-400 and 85. Quick Run. AR@AN for ActivityNet, etc. OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark - mmaction2/README. Prepare a Dataset. For temporal action detection, we implement SSN. By defining a graph with joints as vertices and their natural connections as edges, previous works successfully adopted Graph Convolutional networks (GCNs) to model joint co-occurrences and achieved superior performance. Default: top1_acc. Support five major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, skeleton-based action detection and video retrieval. Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Open source pre-training toolbox based on PyTorch. Update config keys of dict. The values in columns named after "reference" are the results of the original repo. Modeling such visual tempos of different actions facilitates their recognition. Run ‘mim download mmaction2 –config tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb –dest . Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. MMCV . Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark - open-mmlab/mmaction2 Spatio Temporal Action Detection Models¶ ACRN¶. Modify config through script arguments¶ When submitting jobs using tools/train. AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. Abstract¶. For a fair comparison with other models, we Apr 28, 2021 · Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Currently, there are many research works and projects built on MMAction2 by users from community, such as: Video Swin Transformer. mean_average_precision, mmit_mean_average_precision for action recognition dataset (RawframeDataset and VideoDataset). Jul 9, 2023 · Action recognition has garnered much attention and inspired various innovative approaches. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. x version, such as v1. Loading¶ Our primary empirical finding is that pre-training at a very large scale (over 65 million videos), despite on noisy social-media videos and hashtags, substantially improves the state-of-the-art on three challenging public action recognition datasets. MMPreTrain . mmaction2 website . (ActivityNetDataset). Skeleton-based action recognition aims to recognize human actions given human joint coordinates with skeletal interconnections. Action recognition relies on the mmaction2 framework. MMAction implements popular frameworks for action understanding: For action recognition, various algorithms are implemented, including TSN, I3D, SlowFast, R(2+1)D, CSN. Object detection toolbox and benchmark We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. pkl exists as a cache, it contains 6 items as follows:. cviu. You can switch between Chinese and English documents in the lower-left corner of the layout. py. , top1_acc, top5_acc, mean_class_accuracy, mean_average_precision for action recognition dataset (RawframeDataset and VideoDataset). Video demo: A demo script to predict the recognition result using a single video. I really appercaited mmaction2, it's really amazing for quick up a aciton recogition demo, but here I encounted a weired problem Problem I followed the demo/README. 0. Despite the positive results shown in previous works, GCN-based methods are subject to limitations in robustness, interoperability, and scalability. x branch) Prerequisite I have searched Issues and Discussions but cannot get the expected help. md at main · open-mmlab/mmaction2 This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. 0 forks Report repository Releases No releases published. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark - mmaction2/demo/README. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes. I have read the documentation but cannot ge You signed in with another tab or window. Welcome to MMAction2’s documentation! Audio-based Action Recognition Models; Skeleton-based Action Recognition Models; Spatio Temporal Action Detection Models; **Action Recognition in Videos** is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. py or tools/test. Reload to refresh your session. Aug 29, 2023 · Branch main branch (1. Support for multiple action understanding frameworks. MMDetection . 6 top-1 accuracy on Something-Something v2). Despite the positive results shown in previous works, GCN-based methods are subject to limitations in robustness, interoperability, and scalability We report state-of-the-art results on six video action classification and detection datasets, perform detailed ablation studies, and show the generalization of AVSlowFast to learn self-supervised audiovisual features. According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e. [CVPR 2021] TDN: Temporal Difference Networks for Efficient Action Recognition - GitHub - MCG-NJU/TDN: [CVPR 2021] TDN: Temporal Difference Networks for Efficient Action Recognition **Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. Saved searches Use saved searches to filter your results more quickly Support five major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, skeleton-based action detection and video retrieval. 知乎专栏是一个平台，用户可以自由表达观点和分享知识。 open-mmlab / mmaction2 Star 4. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. py, this parameter will auto-scale the learning rate according to the actual batch size and the original batch size. As this framework is heavily focused on Human Action Recognition, the source code was modified and adapted to enable action recognition macaques. Many skeleton-based action recognition methods adopt graph convolutional networks (GCN) to extract features on top of human skeletons. Group convolution has been shown to offer great computational savings in various 2D convolutional architectures for image classification. Outline¶. 08 for 16 GPUs x 4 video/gpu. See full list on github. For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. 103484 Corpus ID: 249940110; Efficient dual attention SlowFast networks for video action recognition @article{Wei2022EfficientDA, title={Efficient dual attention SlowFast networks for video action recognition}, author={Dafeng Wei and Ye Tian and Liqing Wei and Hong Zhong and Siqian Chen and Shiliang Pu and Hongtao Lu}, journal={Comput. You switched accounts on another tab or window. If you want to use a different number of gpus or videos per gpu, the best way is to set --auto-scale-lr when calling tools/train. 08/21 18:16:22 - mmengine - DEBUG - Get class `LocalVisBackend` from "vis_backend" registry in "mmengine" 08/21 18:16:22 - mmengine - DEBUG - An `LocalVisBackend` instance is built from registry, its implementation can be found in mmengine. Inspired by feature selection methods in machine learning, a simple stepwise network expansion approach is employed that expands a single axis in each Options are the evaluation metrics to the test dataset. Get Started. A 20-Minute Guide to MMAction2 FrameWork. RGBPoseConv3D is a framework that jointly uses 2D human skeletons and RGB appearance for human action recognition. 2022. Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 Oral. JHMDB is an action recognition dataset that consists of 960 video sequences belonging to 21 actions. Config System for Action localization. mAP@0. They are trained to predict a fixed set of predefined categories, limiting their transferable ability on new datasets with unseen concepts. This project aims at classifying social interactions into grooming and playing behavior of macaques. open-mmlab/mmaction2 • • CVPR 2017 The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. 知乎专栏为用户提供一个自由表达和随心写作的平台。 **Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 Oral. By default, MMAction2 prefers GPU to CPU. AR@AN, auc for action localization dataset (ActivityNetDataset). 1 watching Forks. To facilitate future research on skeleton action recognition, we also provide a large number of trained models and detailed benchmark results to give some insights. It is noteworthy that the configs we provide are used for 8 gpus as default. It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D Welcome to MMAction2’s documentation! ¶. Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle 欢迎来到 MMAction2 中文教程!¶ You can switch between Chinese and English documents in the lower-left corner of the layout. Inference¶ Run the following command in the root The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. 0, or dev-1. **Skeleton-based Action Recognition** is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. Overview. Visual tempo characterizes the dynamics and the temporal scale of an action. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming. TPN also shows consistent improvements over other challenging baselines on several action recognition datasets. You signed in with another tab or window. md, and run the following demo successfully spatio temporal detection web The gpus indicates the number of gpus we used to get the checkpoint. Inference. It is particularly valuable because it offers a generalized framework for testing The author of C3D normalized UCF-101 with volume mean and used SVM to classify videos, while we normalized the dataset with RGB mean value and used a linear classifier. The content of a pickle file is a dictionary with two fields: split and annotations Split: The value of the split field is a dictionary: the keys are the split names, while the values are lists of video identifiers that belong to the specific clip. 0 rc3 version has brought many new features, including: Latest SOTA video understanding algorithms. The ann_file is a text file with multiple lines, and each line indicates a sample video with the filepath and label, which are split with a whitespace. Stars. [Algorithm] Revisiting Skeleton-based Action Recognition (7 ckpts) [Algorithm] Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (16 ckpts) [Algorithm] PYSKL: Towards Good Practices for Skeleton Action Recognition (8 ckpts) Action Recognition¶ Kinetics-400¶ Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. visualization. Modify Runtime Config. Mar 20, 2023 · There are tasks “Action Classification,” “Video Classification,” and “Self-Supervised Action Recognition” (these are some of the tasks that partially overlap within themselves 1,2,3 Upon the original MMAction2 train and evaluation scripts, we wrote a simple script that combines the training and evalution tools/run. The gpus indicates the number of gpu (32G V100) we used to get the checkpoint. 0 stars Watchers. Config System for Spatio-Temporal Action Detection. The dataset loads raw videos and apply specified transforms to return a dict containing the frame tensors and other information. The data pipeline in MMAction2 is highly adaptable, as nearly every step of the data preprocessing can be configured from the config file. It is a 3D CNN with two streams, with the architecture borrowed from SlowFast. Skeleton-based Action Recognition Models¶ AGCN¶. How to contribute to MMAction2. However, for action recognition in videos, the advantage over traditional methods is not so evident. Training. Enhance skeleton action recognition with rich motion modalities. AR@AN, auc for action localization dataset. gttubes (dict): Dictionary that contains the ground truth tubes for each video. Browse the Dataset. It will discuss some of the recent developments and implementations in action recognition such as 2 stream approaches and transformers. Feb 26, 2019 · **Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. Please provide slow-fast network for skeleton-based action recognition (hopefully with NTU-RGB dataset). The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in Each pickle file corresponds to an action recognition dataset. Modify the Config. The dataset contains video and annotation for puppet flow per frame (approximated optimal flow on the person), puppet mask per frame, joint positions per frame, action label per clip and meta label MMAction2 is an open source toolkit based on PyTorch, supporting numerous video understanding models, including action recognition, skeleton-based action recognition, spatio-temporal action detection and temporal action localization. 5IOU for spatio-temporal action detection dataset (AVADataset). labels (list): List of the 21 labels. MMAction2 is an open-source toolbox for video understanding based on PyTorch. This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. Sep 17, 2021 · The canonical approach to video action recognition dictates a neural model to do a classic and standard 1-of-N majority vote task. Meanwhile, PYSKL supports the training and testing of nine skeleton-based action recognition benchmarks and achieves state-of-the-art recognition performance on eight of them. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. You signed out in another tab or window. Oct 12, 2023 · Discover the possibilities within the "Projects" section and join the vibrant MMAction2 community in pushing the boundaries of video understanding applications! Exciting Features RGBPoseConv3D. Jun 1, 2022 · DOI: 10. Sep 17, 2021 · The canonical approach to video action recognition dictates a neural model to do a classic and standard 1-of-N majority vote task. In this paper, we provide a new perspective on action recognition by attaching importance to the semantic information of Feb 27, 2023 · The latest MMAction2 V1. md at main · open-mmlab/mmaction2 May 29, 2024 · MMAction2, a library provided by OpenMMLab, is specifically designed for activity recognition using deep learning. vis_backend 08/21 18:16:22 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine" 08/21 18:16:22 - mmengine You signed in with another tab or window. py, you may specify --cfg-options to in-place modify the config. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including on action recognition (84. e. Installation. Currently, we only support the testing of VideoMAE models, training will be available soon. 1k. For training and evaluating the whole SOAR model (require the pre-extracted scene label): human action recognition (video merl shopping) This is the code of our team in Datathon 2023 Challenge based on mmaction2 , you can see the demo here: Demo Prepare dataset: 8 code implementations in TensorFlow and PyTorch. In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. 9 top-1 accuracy on Kinetics-600 with ~20xless pre-training data and ~3xsmaller model size) and temporal modeling (69. In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in open-mmlab / mmaction2 Star 4. Description. However, the wide array of options may be overwhelming for some users. This chapter will introduce you to the fundamental functionalities of MMAction2. MMAction2 发布 Revisiting Skeleton-based Action Recognition 论文中所使用的骨架标注。 默认使用 Faster-RCNN 作为人体检测器， 使用 HRNet-w32 作为单人姿态估计模型。 对于 FineGYM 数据集，MMAction2 使用的是运动员的真实框标注，而非检测器所出的框。 mmaction2 based Action Recognition Project upto 76 classes Activity. . Below are some general practices and guidance for building a data pipeline for action recognition tasks. Due to the differences between various versions of Kinetics dataset, there is a little gap between top1/5 acc and mm-Kinetics top1/5 acc. . MMAction2 is an open source toolkit based on PyTorch, supporting numerous video understanding models, including action recognition, skeleton-based action recognition, spatio-temporal action detection and temporal action localization. It is a subset of the larger HMDB51 dataset collected from digitized movies and YouTube videos. , top_k_accuracy, mean_class_accuracy are available for all datasets in recognition, mmit_mean_average_precision for Multi-Moments in Time, mean_average_precision for Multi-Moments in Time and HVU single category. ’ to download the required config. FAQ. In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in Aug 4, 2021 · However, MMAction2 currently only provides slow-only network (config, pretrained file) for skeleton-based action recognition network. 1016/j. Further, we examine three questions in the construction of weakly-supervised video action datasets. If you use mmaction2 as a 3rd-party package, you need to download the conifg and the demo video in the example. 3 code implementations in PyTorch. In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in RGBPoseConv3D is a framework that jointly use 2D human skeletons and RGB appearance for human action recognition. The JHMDB-GT. If you want to train a model on CPU, please empty CUDA_VISIBLE_DEVICES or set it to -1 to make GPU invisible to the program. The values in columns named after "mm-Kinetics" are the testing results on the Kinetics dataset held by MMAction2, which is also used by other models in MMAction2. , lr=0. Modify Model Config. Modify Dataset. We will release the dataset publicly. Note. Config System for Action Recognition. For spatial temporal atomic action detection, a Fast-RCNN baseline is Video dataset for action recognition. Modify configs through script arguments: Tricks to directly modify configs through script arguments. This section will look at works that are similar to the study that is carried out in this paper. 01 for 4 GPUs x 2 video/gpu and lr=0. OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark - open-mmlab/mmaction2 In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Foundational library for computer vision. [1] The models are ported from the repo VideoMAE and tested on our data. db oy bw dp va to xu rn tu ux