Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.
You don't have to limit yourself to a binary classifier of course; CNNs can easily scale to thousands of different classes, as seen in the well-known ImageNet dataset of 1000 classes, used to benchmark computer vision algorithm performance.
GET /api/FastapiGET/pip install Tensorflow==2.7.0GET/HTML , CSS , JSGET/pip install FastapiI have used Hardcoding technique for this model as I wanted some practice with tensorflow and keras.
The Dataset had 3000 images in toatal which were then segregated in 80%-20% train-test segmentation.
Then the train dataset is split into 70%-30% train-validation segmentation.
I have used DataAugmentation to increase the varaition of images in the dataset.
The main Model consists of 16 layers from which 6 are combination of Conv2D & MaxPooling2D.
After this we test the model with the test images and save the model in Saved_Models
The Application is hosted on https://localhost:8080/predict server.
To run the model Run the main.py file from API folder and then run the main.html file from the frontend folder
Now upload the image and click predict
You will see the Prediction as well as the confidence of its prediction.
After tuning a hard-coded model the accuracy of the model has reached till 98%