https://github.com/amir22010/traffic-net

A dataset of traffic, fire and accident images for training deep learning models.

https://github.com/amir22010/traffic-net

Repository

A dataset of traffic, fire and accident images for training deep learning models.

https://github.com/Amir22010/Traffic-Net/blob/master/

# Traffic-Net
Traffic-Net is a dataset containing images of dense traffic, sparse traffic, accidents and burning vehicles.





Traffic-Net is a dataset of traffic images, collected in order to ensure that machine learning systems can be trained
 to detect traffic conditions and provide real-time monitoring, analytics and alerts. 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 Traffic-Net dataset. It contains 4,400 images that span cover 4 classes. The classes
  included in this release are: 



  -  Accident  

  -  Dense Traffic  

  -  Fire  

  -  Sparse Traffic  


  There are 1,100 images for each category, with 900 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 Traffic-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/Traffic-Net/releases/tag/1.0  



 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 traffic_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/Traffic-Net/releases/download/1.0/trafficnet_resnet_model_ex-055_acc-0.913750.h5

  

   This pre-trained model was trained for **60 epochs** only, but it achieved over **91%** accuracy on 800 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. 







  
Sparse_Traffic  :  99.98759031295776
Accident  :  0.006892996316310018
Dense_Traffic  :  0.0031178133212961257
Fire  :  0.0023975149815669283






Dense_Traffic  :  100.0
Accident  :  9.411973422857045e-07
Fire  :  2.656607822615342e-07
Sparse_Traffic  :  4.631924704900925e-09







Accident  :  99.94832277297974
Sparse_Traffic  :  0.04670554480981082
Fire  :  0.004610423275153153
Dense_Traffic  :  0.00035401615150476573







Fire  :  100.0
Accident  :  1.9869084979303675e-22
Dense_Traffic  :  3.262699368229192e-23
Sparse_Traffic  :  6.003136426033551e-28






References

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

 https://arxiv.org/abs/1512.03385 

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