Welcome to the Dog Breed Classification Project! This project aims to accurately identify the breed of a dog from an image using a powerful deep-learning model. The model is trained with advanced techniques to achieve high accuracy, making it a reliable tool for dog breed identification.

• ML Model: This four-legged breed identifier is powered and trained by the incredible PyTorch.

• Accuracy Extraordinaire: Our model boasts a whopping 97.54% accuracy.

• Breeds Bonanza: Trained with love on 30 diverse breeds from across the globe.ation: Utilizes weather data to optimize plant care schedules.

• Framework: PyTorch
• Model Architecture: ResNet-50
• Training Technique: Transfer Learning

• model_training.py: This script contains the code for training the model. It handles data preprocessing, model training, and evaluation.

• prediction_dog.py: This script is used to test the model. You can classify a new dog image by running this script. Ensure you update the image path in the script to point to your dog image.

• model_checkpoint_epoch_6.pth: This is the latest model file, containing the trained weights of the model. You can use this file to load the model and make predictions. Usage

Run model_training.py to train the model on your dataset. Make sure you have the necessary data and dependencies set up.

Use prediction_dog.py to classify a new dog image. Change the image path in the script to the path of the dog image you want to classify.

Check out the production version of the model in action at Pupteller

This link: https://pupteller.netlify.app/ will take you to a web application where you can upload an image of a dog, and it will predict the breed using the trained model.

Feel free to contribute to this project by submitting a pull request. For major changes, please open an issue first to discuss what you would like to change.