sauravsolanki/image-classification

Image Classification

Train a neural network model on the collected data Dataset consists of 2400 total images distributed across the training, validation and test set.

Dataset:

Dataset consists of 3 folders Training, Validation and Testing which have 6 different classes:

1. Bicycle
2. Boat
3. Cat
4. Motorbike
5. People
6. Table

Dataset Distribution:

# of Image	Part
1447	Training
487	Validation
481	Testing

# of Training Image	Class
241	Table
241	People
241	Motorbike
241	Cat
241	Boat
241	Bicycle

# of Validation Image	Class
81	Table
81	People
81	Motorbike
81	Cat
81	Boat
81	Bicycle

# of Testing Image
480

| -- Train
        | -- images
        | -- masks
| -- Test
        | -- images
        | -- masks

Model:

1. Transfer Learning: Trained on Multiple-backbone Model of input-dim: (224,224,3)

2. Jupyter Notebook, Kaggle-training.ipyb: Model Trained and Inference in Kaggle GPU Notebook

3. Results:

Inference Image

Requirements

Python 3.7.8, TensorFlow 2.5, and other common packages listed in requirements.txt.

Installation

1. Clone this repository
2. Install dependencies pip3 install -r requirements.txt
3. Run setup from the repository root directory python3 setup.py install