README.md · wftdly001/Traffic-Net-growuai

# Traffic-Net
Traffic-Net is a dataset containing images of dense traffic, sparse traffic, accidents and burning vehicles.
 
<img src="images/traffic_net.jpg" />
<hr>
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 <a href="https://deepquestai.com" >DeepQuest AI</a>'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. <a href="https://github.com/OlafenwaMoses/Traffic-Net/releases/tag/1.0" >https://github.com/OlafenwaMoses/Traffic-Net/releases/tag/1.0</a>

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 <a href="traffic_net.py" >traffic_net.py</a> file and the model class labels for prediction is also provided the 
 <a href="model_class.json" >model_class.json</a>. The pretrained ResNet50 model is available for download via the link below. 
 <a href="https://github.com/OlafenwaMoses/Traffic-Net/releases/download/1.0/trafficnet_resnet_model_ex-055_acc-0.913750.h5" >https://github.com/OlafenwaMoses/Traffic-Net/releases/download/1.0/trafficnet_resnet_model_ex-055_acc-0.913750.h5</a> 
 
 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) <a href="https://www.tensorflow.org/install/install_windows" style="text-decoration: none;" > Install</a> or install via pip <pre> pip3 install --upgrade tensorflow </pre> 
 
 - OpenCV <a href="https://pypi.python.org/pypi/opencv-python" style="text-decoration: none;" >Install</a> or install via pip <pre> pip3 install opencv-python </pre> 
 
 - Keras 2.x <a href="https://keras.io/#installation" style="text-decoration: none;" >Install</a> or install via pip <pre> pip3 install keras </pre> 
 
 - ImageAI 2.0.3 
 <pre>pip3 install imageai </pre>

>>> Video & Prediction Results 
Click below to watch the video demonstration of the trained model at work. 
<a href="https://www.youtube.com/watch?v=PupK_qd3bP0" ><img src="images/video_image.jpg" /></a>
 
 <img src="images/1.jpg" />
<pre>
Sparse_Traffic : 99.98759031295776
Accident : 0.006892996316310018
Dense_Traffic : 0.0031178133212961257
Fire : 0.0023975149815669283
</pre>

<hr>
 
<img src="images/2.jpg" />
<pre>
Dense_Traffic : 100.0
Accident : 9.411973422857045e-07
Fire : 2.656607822615342e-07
Sparse_Traffic : 4.631924704900925e-09
</pre>

<hr>

<img src="images/3.jpg" />
<pre>
Accident : 99.94832277297974
Sparse_Traffic : 0.04670554480981082
Fire : 0.004610423275153153
Dense_Traffic : 0.00035401615150476573
</pre>

<hr>

<img src="images/4.jpg" />
<pre>
Fire : 100.0
Accident : 1.9869084979303675e-22
Dense_Traffic : 3.262699368229192e-23
Sparse_Traffic : 6.003136426033551e-28
</pre>

<h3>References</h3>

1. Kaiming H. et al, Deep Residual Learning for Image Recognition 
 <a href="https://arxiv.org/abs/1512.03385" >https://arxiv.org/abs/1512.03385</a>

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