A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.

Related tags

Deep Learning pytorch-implmention hcn skeleton-based-action-recognition nturgb-d
Overview

A PyTorch Reproduction of HCN

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu, IJCAI 2018.

Arxiv Preprint

Features

1. Dataset

  • NTU RGB+D: Cross View (CV), Cross Subject (CS)
  • SBU Kinect Interaction
  • PKU-MMD

2. Tasks

  • Action recognition
  • Action detection

3. Visualization

  • Visdom supported.

Prerequisites

Our code is based on Python3.5. There are a few dependencies to run the code in the following:

  • Python >= 3.5
  • PyTorch == 0.4.0
  • torchnet
  • Visdom
  • Other version info about some Python packages can be found in requirements.txt

Usage

Data preparation

NTU RGB+D

To transform raw NTU RGB+D data into numpy array (memmap format ) by this command:

python ./feeder/ntu_gendata.py --data_path <path for raw skeleton dataset> --out_folder <path for new dataset>
Other Datasets

Not supported now.

Training

Before you start the training, you have to launch visdom server.

python -m visdom

To train the model, you should note that:

  • --dataset_dir is the parents path for all the datasets,
  • --num the number of experiments trials (type: list).
python main.py --dataset_dir <parents path for all the datasets> --mode train --model_name HCN --dataset_name NTU-RGB-D-CV --num 01

To run a new trial with different parameters, you need to:

  • Firstly, run the above training command with a new trial number, e.g, --num 03, thus you will got an error.
  • Secondly, copy a parameters file from the ./HCN/experiments/NTU-RGB-D-CV/HCN01/params.json to the path of your new trial "./HCN/experiments/NTU-RGB-D-CV/HCN03/params.json" and modify it as you want.
  • At last, run the above training command again, it will works.

Testing

python main.py --dataset_dir <parents path for all the datasets> --mode test --load True --model_name HCN --dataset_name NTU-RGB-D-CV --num 01

Load and Training

You also can load a half trained model, and start training it from a specific checkpoint by the following command:

python main.py --dataset_dir <parents path for all the datasets> --mode load_train --load True --model_name HCN --dataset_name NTU-RGB-D-CV --num 01 --load_model <path for  trained model>

Results

Table

The expected Top-1 accuracy of the model for NTU-RGD+D are shown here (There is an accuracy gap. I am not the author of original HCN paper, the repo was reproduced according to the paper text and have not been tuned carefully):

Model Normalized
Sequence
Length		 FC
Neuron
Numbers		 NTU RGB+D
Cross Subject (%)		 NTU RGB+D
Cross View (%)
HCN[1] 32 256 86.5 91.1
HCN 32 256 84.2 89.2
HCN 64 512 84.9* 90.9*

[1] http://arxiv.org/pdf/1804.06055.pdf

Figures

  • Loss & accuracy[CV]

Confusion matrix

- Loss & accuracy[CS]

Reference

[1] Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu. Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. IJCAI 2018.

[2] yysijie/st-gcn: referred for some code of dataset processing.

Comments
  • Difference in reported accuracy

    Difference in reported accuracy

    Hi, I ran you experiemnt in following setting

    HCN[1] | 32 | 256 -- | -- | -- However, my accuracy is only 83.8 for cross subject evaluation.

    Also for

    HCN[1] | 32 | 512 -- | -- | -- my accuracy is only 84.5 for cross subject evaluation.

    opened by fmthoker 9
  • How to deal with absent data

    How to deal with absent data

    Hello, i have a question:

    If my data are absent,(for example, the skeletons for some frames are absent or some joints are absent in a frame ), how to deal with these cases.

    Thanks!

    opened by pyni 5
  • demo？

    demo？

    I implemented a skeleton extraction, and then motion recognition demo； I modified the use of a smaller number of key points （14）for motion recognition, retraining，there are 6 classes。The training accuracy was 0.90102261。 also I modified the network parameters, window size is 16, person num is 1。 But demo's action recognition is poor. I accumulated 16 frames of input into the HCN, the next time from the previous accumulation of half the beginning of the accumulation。

    Please give me some advice, thank you

    opened by lunalulu 3
  • used 2D skeleton dataset??

    used 2D skeleton dataset??

    Keeping other unchanged, it is modified to use 2D skeleton data as input for training, and the best model after 3D skeleton training is used as the pre-training model. However, the accuracy after training is very low, less than 2%. Is there any method to improve the accuracy of 2D skeleton input? Thank you very much

    opened by lunalulu 3
  • metrics_mean = {metric: np.mean([x[metric] for x in summ]) for metric in summ[0]}

    metrics_mean = {metric: np.mean([x[metric] for x in summ]) for metric in summ[0]}

    Hello， I met a problem with this line. The error is: File "/appl/soft/ai/miniconda3/envs/pytorch-1.3.0-1/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 3257, in mean out=out, **kwargs) File "/appl/soft/ai/miniconda3/envs/pytorch-1.3.0-1/lib/python3.7/site-packages/numpy/core/_methods.py", line 161, in _mean ret = ret.dtype.type(ret / rcount) AttributeError: 'torch.dtype' object has no attribute 'type'

    I did try torch.mean(torch.stack()), however it still not work. Do you have some seggestions?

    opened by xiaoiker 2
  • RuntimeError: cuda runtime error (710) :

    RuntimeError: cuda runtime error (710) :

    python main.py --dataset_dir --mode train --model_name HCN --dataset_name NTU-RGB-D-CV --num 01 I got RuntimeError: cuda runtime error (710) during training. PyTorch version=1.4.0

    2020-02-15 23:45:01,865:INFO: seed: 0 2020-02-15 23:45:01,865:INFO: batch_size: 64 2020-02-15 23:45:01,866:INFO: scheduler_gamma3: 0.5 2020-02-15 23:45:01,866:INFO: lr_step: [100, 160, 200] 2020-02-15 23:45:01,866:INFO: gpu_id: 0 2020-02-15 23:45:01,866:INFO: lr_decay_type: exp 2020-02-15 23:45:01,867:INFO: patience: 20 2020-02-15 23:45:01,867:INFO: test_feeder_args: {'window_size': 32, 'normalization': False, 'random_valid_choose': False, 'random_shift': False, 'p_interval': [0.95], 'origin_transfer': 0, 'debug': False, 'data_path': None, 'random_move': False, 'crop_resize': True, 'label_path': None} 2020-02-15 23:45:01,867:INFO: model_version: HCN 2020-02-15 23:45:01,867:INFO: dataset_name: NTU-RGB-D-CV 2020-02-15 23:45:01,868:INFO: data_parallel: False 2020-02-15 23:45:01,868:INFO: optimizer: Adam 2020-02-15 23:45:01,868:INFO: restore_file: None 2020-02-15 23:45:01,868:INFO: model_args: {'window_size': 32, 'num_class': 60, 'num_person': 2, 'num_joint': 25, 'in_channel': 3, 'out_channel': 64} 2020-02-15 23:45:01,869:INFO: loss_args: {'type': 'CE'} 2020-02-15 23:45:01,869:INFO: scheduler_gamma: 0.1 2020-02-15 23:45:01,869:INFO: dataset_dir: /home/ashish/Documents/BTP/HCN-pytorch-master/feeder/data/ 2020-02-15 23:45:01,869:INFO: start_epoch: 0 2020-02-15 23:45:01,869:INFO: num_epochs: 400 2020-02-15 23:45:01,870:INFO: weight_decay: 0.0001 2020-02-15 23:45:01,870:INFO: lr: 0.001 2020-02-15 23:45:01,870:INFO: num_workers: 4 2020-02-15 23:45:01,870:INFO: clip: 0.5 weight initial finished! 2020-02-15 23:45:03,664:INFO: HCN( (conv1): Sequential( (0): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU() ) (conv2): Conv2d(64, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv3): Sequential( (0): Conv2d(25, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv4): Sequential( (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Dropout2d(p=0.5, inplace=False) (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv1m): Sequential( (0): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU() ) (conv2m): Conv2d(64, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)) (conv3m): Sequential( (0): Conv2d(25, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv4m): Sequential( (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Dropout2d(p=0.5, inplace=False) (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv5): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU() (2): Dropout2d(p=0.5, inplace=False) (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv6): Sequential( (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU() (2): Dropout2d(p=0.5, inplace=False) (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc7): Sequential( (0): Linear(in_features=1024, out_features=512, bias=True) (1): ReLU() (2): Dropout2d(p=0.5, inplace=False) ) (fc8): Linear(in_features=512, out_features=60, bias=True) ) 2020-02-15 23:45:03,665:INFO: Loading the datasets... 2020-02-15 23:45:03,945:INFO: - done. 2020-02-15 23:45:03,945:INFO: Starting training for 400 epoch(s) 2020-02-15 23:45:03,945:INFO: lr decay:exp /home/ashish/anaconda3/lib/python3.5/site-packages/torch/optim/lr_scheduler.py:122: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) 2020-02-15 23:45:03,946:INFO: Epoch 1/400 0%| | 0/1 [00:00<?, ?it/s]/home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/functional.py:2416: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead. warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") /home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/functional.py:2416: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead. warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [0,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [1,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [2,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [3,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [4,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [5,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [6,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [7,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [8,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [9,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [10,0,0] Assertion t >= 0 && t < n_classes failed. /pytorch/aten/src/THCUNN/ClassNLLCriterion.cu:106: void cunn_ClassNLLCriterion_updateOutput_kernel(Dtype *, Dtype *, Dtype *, long *, Dtype *, int, int, int, int, long) [with Dtype = float, Acctype = float]: block: [0,0,0], thread: [11,0,0] Assertion t >= 0 && t < n_classes failed. THCudaCheck FAIL file=/pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu line=110 error=710 : device-side assert triggered 0%| | 0/1 [00:00<?, ?it/s] Traceback (most recent call last): File "main.py", line 522, in args.model_dir,logger, params.restore_file) File "main.py", line 319, in train_and_evaluate train_metrics,train_confusion_meter = train(model, optimizer, loss_fn, train_dataloader, metrics, params,logger) File "main.py", line 87, in train loss_bag = loss_fn(output_batch,labels_batch,current_epoch=params.current_epoch, params=params) File "/home/ashish/Documents/BTP/HCN-pytorch-master/model/HCN.py", line 151, in loss_fn CE = nn.CrossEntropyLoss()(outputs, labels) File "/home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "/home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/modules/loss.py", line 916, in forward ignore_index=self.ignore_index, reduction=self.reduction) File "/home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/functional.py", line 2021, in cross_entropy return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) File "/home/ashish/anaconda3/lib/python3.5/site-packages/torch/nn/functional.py", line 1838, in nll_loss ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: cuda runtime error (710) : device-side assert triggered at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:110

    opened by ashishhacker 2
  • download NTU RGB+D dataset？？

    download NTU RGB+D dataset？？

    Thanks for the work you've done！ Due to an error of the NUT data set server, I have not applied for the download of the data set. Could you please send me the data set?

    thanks very much~

    opened by lunalulu 2
  • the download of dataset

    the download of dataset

    ntu I have a simple problem about dataset download. the total dataset is 1.3TB, it means we need download all the datasets ? or only part of them ,such as 3D skeletons(5.8GB) Thanks!

    opened by Tylerjoe 2
  • Higher test set accuracy?

    Higher test set accuracy?

    Thank you for this nice repo and congrats. for your nice results. I have some questions about your repo.

    ~~As stated in #2, the test set accuracy is significantly higher than training accuracy during nearly first 100 epochs. Is it because of dropouts or any other reason?~~

    ~~Also, my second question is: are there any difference between eval accuracy during training mode and accuracy stated in --mode test? They are the same right?~~

    My other question is: what is the difference between HCN[1] and HCN. Aren't they the same network with same configuration?

    HCN[1] | 32 | 256 | 86.5 | 91.1 -- | -- | -- | -- | -- HCN | 32 | 256 | 84.2 | 89.2

    Edit: I understand the first two questions, so I disabled them.

    opened by cagbal 2
  • Only using 2 people skeleton sequence?

    Only using 2 people skeleton sequence?

    Hi,

    I want to ask that are you only using 2 people skeleton sequence on training and testing? If I want to use the data include 1 person and multi-people, how can I do?

    Thanks!

    opened by Peter654q 2
  • Bump nbconvert from 5.3.1 to 6.3.0

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  • Bump pillow from 5.1.0 to 9.3.0

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    • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

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    • Inline fname2char to fix memory leak #6329 [nulano]

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  • Bump nbconvert from 5.3.1 to 6.5.1

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    dependencies 
    opened by dependabot[bot] 0
  • Bump mistune from 0.8.3 to 2.0.3

    Bump mistune from 0.8.3 to 2.0.3

    Bumps mistune from 0.8.3 to 2.0.3.

    Release notes

    Sourced from mistune's releases.

    Version 2.0.2

    Fix escape_url via lepture/mistune#295

    Version 2.0.1

    Fix XSS for image link syntax.

    Version 2.0.0

    First release of Mistune v2.

    Version 2.0.0 RC1

    In this release, we have a Security Fix for harmful links.

    Version 2.0.0 Alpha 1

    This is the first release of v2. An alpha version for users to have a preview of the new mistune.

    Version 0.8.4

    • Support an escaped pipe char in a table cell. #150
    • Fix ordered and unordered list. #152
    • Fix spaces between = in HTML tags
    • Add max_recursive_depth for list and blockquote.
    • Fix fences code block.
    Changelog

    Sourced from mistune's changelog.

    Changelog

    Here is the full history of mistune v2.

    Version 2.0.4

    
Released on Jul 15, 2022

      

    • Fix url plugin in &lt;a&gt; tag
      • 

    • Fix * formatting
      • 

    

    Version 2.0.3

    Released on Jun 27, 2022

    • Fix table plugin
    • Security fix for CVE-2022-34749

    Version 2.0.2

    
Released on Jan 14, 2022

    Fix escape_url
    

    Version 2.0.1

    Released on Dec 30, 2021

    XSS fix for image link syntax.

    Version 2.0.0

    
Released on Dec 5, 2021

    This is the first non-alpha release of mistune v2.
    

    Version 2.0.0rc1

    Released on Feb 16, 2021

    Version 2.0.0a6

    
</tr></table>

    ... (truncated)

    Commits
    • 3f422f1 Version bump 2.0.3
    • a6d4321 Fix asteris emphasis regex CVE-2022-34749
    • 5638e46 Merge pull request #307 from jieter/patch-1
    • 0eba471 Fix typo in guide.rst
    • 61e9337 Fix table plugin
    • 76dec68 Add documentation for renderer heading when TOC enabled
    • 799cd11 Version bump 2.0.2
    • babb0cf Merge pull request #295 from dairiki/bug.escape_url
    • fc2cd53 Make mistune.util.escape_url less aggressive
    • 3e8d352 Version bump 2.0.1
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Bump numpy from 1.14.3 to 1.22.0

    Bump numpy from 1.14.3 to 1.22.0

    Bumps numpy from 1.14.3 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

    Commits

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    dependencies 
    opened by dependabot[bot] 0
  • Bump notebook from 5.5.0 to 6.4.12

    Bump notebook from 5.5.0 to 6.4.12

    Bumps notebook from 5.5.0 to 6.4.12.

    Dependabot compatibility score

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    dependencies 
    opened by dependabot[bot] 0
  • Official implementation of HCN

    Official implementation of HCN

    Hi All,

    Thank @huguyuehuhu and other contributors for the excellent implementation of HCN. Here is good news! We finally released the official souce code of HCN at hikvision-research/skelact, along with some new works from our lab. The HCN model was originally implemented in TensorFlow and reimplemented in PyTorch. The performance of HCN matches with the paper (with slightly different implementation details). Please check the SkelAct repo, and we welcome contributions from the community.

    enhancement 
    opened by xiaoyong 1
Owner
Guyue Hu
Guyue Hu
GitHub
Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

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