https://github.com/olafenwamoses/action-net

A dataset of images for artificial intelligence models to recognize human actions.
https://github.com/olafenwamoses/action-net

Last synced: 8 months ago
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A dataset of images for artificial intelligence models to recognize human actions.

• Host: GitHub
• URL: https://github.com/olafenwamoses/action-net
• Owner: OlafenwaMoses
• License: mit
• Created: 2019-07-11T10:15:16.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-07-12T17:41:17.000Z (about 6 years ago)
• Last Synced: 2025-01-13T14:48:13.364Z (9 months ago)
• Language: Python
• Size: 1.66 MB
• Stars: 52
• Watchers: 7
• Forks: 31
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Action-Net

Action-Net is a dataset containing images of 16 different human actions.









Action-Net is a dataset containing images of human actions , collected in order to ensure that machine learning systems can be trained

 to understand human actions, gestures and activities. This is part of DeepQuest AI's to train machine learning systems to 

  perceive, understand and act accordingly in solving problems in any environment they are deployed. 



  This is the first release of the Action-Net dataset. It contains 19,200 images that span cover 16 classes. The classes

  included in this release are: 



  -  Calling  


  -  Clapping  


  -  Cycling  


  -  Dancing  


  -  Drinking  


  -  Eating  


  -  Fighting  


  -  Hugging  


  -  Kissing  


  -  Laughing  


  -  Listening to Music  


  -  Running  


  -  Sitting  


  -  Sleeping  


  -  Texting  


  -  Using Laptop  


  

  There are 1,200 images for each category, with 1000 images for trainings  and 200 images for testing . We are working on adding more

   categories in the future and will continue to improve the dataset.

  

 


  >>> DOWNLOAD, TRAINING AND PREDICTION:  



 The Action-Net dataset is provided for download in the release section of this repository.

 You can download the dataset via the link below.

 https://github.com/OlafenwaMoses/Action-Net/releases/download/v1/action_net_v1.zip  



 We have also provided a python codebase to download the images, train ResNet50 on the images

  and perform prediction using a pretrained model (also using ResNet50) provided in the release section of this repository.

  The python codebase is contained in the action_net.py file and the model class labels for prediction is also provided the 

  model_class.json. The pretrained ResNet50 model is available for download via the link below. 

 

  https://github.com/OlafenwaMoses/Action-Net/releases/download/v1/action_net_ex-060_acc-0.745313.h5


  


   This pre-trained model was trained for **60 epochs** only, but it achieved over **74%** accuracy on 3200 test images. You can see the prediction results on new images that were not part of the dataset in the **Prediction Results** section below. More experiments will enhance the accuracy of the model.




Running the experiment or prediction requires that you have **Tensorflow**, and **Keras**, **OpenCV** and **ImageAI** installed. You can install this dependencies via the commands below.


- Tensorflow 1.4.0 (and later versions)         Install or install via pip 
 pip3 install --upgrade tensorflow 
 

       

  - OpenCV          Install or install via pip 
 pip3 install opencv-python 
 

       

   - Keras 2.x       Install or install via pip 
 pip3 install keras 
 

  

   - ImageAI 2.0.3    

         
pip3 install imageai  

 


>>> Video & Prediction Results 



Click below to watch the video demonstration of the trained model at work. 










  


eating  :  100.0

drinking  :  3.92037860508232e-09

using-laptop  :  6.944534465709584e-11

calling  :  5.7910951424891555e-12











eating  :  99.44907426834106

drinking  :  0.5508399568498135

using-phone  :  5.766927415606915e-05

sitting  :  1.1222620344142342e-05











fighting  :  99.97442364692688

running  :  0.01658390392549336

dancing  :  0.008970857743406668

sitting  :  7.210289965087213e-06











laughing  :  99.99998807907104

clapping  :  1.3144966715117334e-05

calling  :  4.0294068526236515e-06

eating  :  4.981405066217803e-07











running  :  99.99852180480957

calling  :  0.0009251662959286477

listening-to-music  :  0.0002909338491008384

cycling  :  0.00024121977730828803






References


 

 1. Kaiming H. et al, Deep Residual Learning for Image Recognition 


 https://arxiv.org/abs/1512.03385