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https://github.com/abhash-rai/traffic-image-classifier
A web-based solution utilizing a robust tensorflow model for precise traffic condition classification made in ReactJs and FastAPI for backend.
https://github.com/abhash-rai/traffic-image-classifier
cnn cnn-classification cnn-keras cnn-model data-analysis data-science data-visualization fastapi keras keras-tensorflow python python-3 python3 react reactjs tensorflow traffic traffic-classification transfer-learning
Last synced: 17 days ago
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A web-based solution utilizing a robust tensorflow model for precise traffic condition classification made in ReactJs and FastAPI for backend.
- Host: GitHub
- URL: https://github.com/abhash-rai/traffic-image-classifier
- Owner: abhash-rai
- License: mit
- Created: 2023-10-22T15:30:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-16T13:34:37.000Z (about 1 year ago)
- Last Synced: 2024-11-15T19:34:40.566Z (3 months ago)
- Topics: cnn, cnn-classification, cnn-keras, cnn-model, data-analysis, data-science, data-visualization, fastapi, keras, keras-tensorflow, python, python-3, python3, react, reactjs, tensorflow, traffic, traffic-classification, transfer-learning
- Language: JavaScript
- Homepage:
- Size: 10.6 MB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://www.kaggle.com/code/abhashrai/traffic-congestion-prediction-cnn-xception/).
# Table of Content
- [Prerequisites](#prerequisites)
- [Demo](#demo)
- [Usage](#usage)
- [Model Summary](#model-summary)
- [Model Evaluation](#model-evaluation)
- [Acknowledgement](#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
```Python
_________________________________________________________________
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:
```Python
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
```
```Python
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:
```Python
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
```
```Python
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](https://www.linkedin.com/in/ashalimbu/) or [GitHub](https://github.com/asha-limbu)