traffic-image-classifier

TASKS PERFORMED 🡆 Data Collection (Manual, Web Scrapping)✅, Exploratory Data Analysis✅, Data Augmentation✅, Model Training (ML Algorithms, CNN, Transfer Learning)✅, Hyperparameter Tuning✅, Model Validation✅, Deployment (UI Design, React and FastAPI)✅

Upload images and make classification on:

• traffic_unrelated: Indicates an absence of traffic related situation
• congested_traffic: Indicates complete standstill caused by congestion or blockages or accidents
• heavy_traffic: Indicates high volume of vehicles causing slow moving traffic conditions
• moderate_traffic: Indicates moderate or steady flow of traffic with moderate number of vehicles
• light_traffic: Indicates minimal or very few vehicles on the road

For more details on the model training process, please visit the Kaggle project here.

Table of Content

• Prerequisites
• Demo
• Usage
• Model Summary
• Model Evaluation
• Acknowledgement

Prerequisites

• You must have Python 3 installed on your system.
• You must have Git installed on your system.
• You must have Git LFS installed on your system (for cloning/downloading pre-trained model file).
• (Optional) You should have Node.js (npm) installed on your system (if using development server)

Demo

Step 1: Upload an image ↓

Step 2: Crop the image into square ratio ↓

Step 3: Get the results ↓

Usage

1. Clone the repository (In terminal):

   git clone https://github.com/AbhashChamlingRai/traffic-image-classifier.git
   

2. Enter into the project directory:

   cd traffic-image-classifier
   

   Before following the below procedure, go to this link and download the model traffic_classifier.h5 and place it into the /traffic-image-classifier directory.

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Start Fast-API:

   Open /fastapi directory in your terminal. Then run the below command and wait until you see the message Application startup complete. in your terminal:

   uvicorn main:app --port 8000 --reload
   

5. Run React App:

   Open /react-app-build/index.html file in your browser. Upload images in the web app and make predictions.

6. (Optional) If you want to run development server:

   Open /react-app-development directory in your terminal. Then install project dependencies using npm install command. Wait until installation process completes, then run the development server by running the command npm start.

Model Summary

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_4 (InputLayer)        [(None, 300, 300, 3)]     0         
                                                                 
 tf.cast_1 (TFOpLambda)      (None, 300, 300, 3)       0         
                                                                 
 tf.math.truediv_1 (TFOpLamb  (None, 300, 300, 3)      0         
 da)                                                             
                                                                 
 tf.math.subtract_1 (TFOpLam  (None, 300, 300, 3)      0         
 bda)                                                            
                                                                 
 xception (Functional)       (None, 2048)              20861480  
                                                                 
 batch_normalization_11 (Bat  (None, 2048)             8192      
 chNormalization)                                                
                                                                 
 dense_3 (Dense)             (None, 368)               754032    
                                                                 
 batch_normalization_12 (Bat  (None, 368)              1472      
 chNormalization)                                                
                                                                 
 dense_4 (Dense)             (None, 112)               41328     
                                                                 
 batch_normalization_13 (Bat  (None, 112)              448       
 chNormalization)                                                
                                                                 
 dropout_1 (Dropout)         (None, 112)               0         
                                                                 
 dense_5 (Dense)             (None, 5)                 565       
                                                                 
=================================================================
Total params: 21,667,517
Trainable params: 800,981
Non-trainable params: 20,866,536
_________________________________________________________________

The model needs input of a 300x300 pixel color image represented a NumPy array with RGB (3 channels). So, the input array must be of shape (1, 300, 300, 3). You don't have to worry about normalizing the pixel values (scaling by 1/255) because the first four layers of the model take care of it.

It generates prediction confidence scores represented by 5 consecutive numbers, each corresponding to a specific class: congested_traffic, heavy_traffic, light_traffic, moderate_traffic, and traffic_unrelated (in the specified order).

Model Evaluation

The data was partitioned into two sets for training, and validation, each containing samples from five distinct classes. The split ratios are as follows: 85% for training, 15% for validation. Despite the limited data, the model successfully met my expectations:

For validation data:

score, acc = model.evaluate(validation_generator)
print('Test Loss =', score)
print('Test Accuracy =', acc)

Output:
8/8 [==============================] - 12s 1s/step - loss: 0.1525 - accuracy: 0.9468
Test Loss = 0.15245917439460754
Test Accuracy = 0.9468421339988708

Confusion Matrix:
[[350  15   1  11   1]
 [ 14 290   1  15   0]
 [  1   1 345  11   2]
 [  5  12   2 332   0]
 [  4   0   5   0 482]]

Classification Report:
              precision    recall  f1-score   support

           0       0.94      0.93      0.93       378
           1       0.91      0.91      0.91       320
           2       0.97      0.96      0.97       360
           3       0.90      0.95      0.92       351
           4       0.99      0.98      0.99       491

    accuracy                           0.95      1900
   macro avg       0.94      0.94      0.94      1900
weighted avg       0.95      0.95      0.95      1900

For train data:

score, acc = model.evaluate(train_generator)
print('Test Loss =', score)
print('Test Accuracy =', acc)

Output:
43/43 [==============================] - 239s 6s/step - loss: 0.0883 - accuracy: 0.9695
Test Loss = 0.08829263597726822
Test Accuracy = 0.9695025682449341

Confusion Matrix:
[[2055   48   11   21    3]
 [  57 1711    5   36    1]
 [   4    3 2001   27    5]
 [  18   35   14 1920    2]
 [   3    1    4    4 2766]]

Classification Report:
              precision    recall  f1-score   support

           0       0.96      0.96      0.96      2138
           1       0.95      0.95      0.95      1810
           2       0.98      0.98      0.98      2040
           3       0.96      0.97      0.96      1989
           4       1.00      1.00      1.00      2778

    accuracy                           0.97     10755
   macro avg       0.97      0.97      0.97     10755
weighted avg       0.97      0.97      0.97     10755

Acknowledgement

Special thanks to Asha Limbu for her work in creating the UI/UX design.

• Connect with Asha on LinkedIn or GitHub