Dog Breed Recognition

Description

Develop a simple dog breed recognition system. However, with an extra difficulty: adding races that weren't seen in training time, and knowing how to identify unknowns (for example, our dear mutts).

Requirements

To proper run this repo you need to have installed the following packages:

• Python 3.6 or superior
• Matplotlib
• Numpy
• Scikit-Learn
• PyTorch
• Torchvision

Dataset

Dog Breeds dataset contains image from 100 distinct ldog-breeds to train a model. Additionally, it provides a subset for enroll new classes to the trained model and a final test set to evaluate the enroll.

Download the dog breeds dataset here.

Pre-trained model

Download original 100 dog breeds recognition model weights available here.

Download enrolled 20 dog breeds recognition model weights available here.

Instructions

There are basically 3 main operations to perform using this repo:

• train: train a dog breed recognition model from scratch
• enroll: replace the original dog breeds and enroll new ones to the model
• eval: evaluate the enrolled breeds

Train

To re-train the model, run the following command:

python train.py <dirpath> <outpath> --epochs <num_epochs> --learning-rate <learning_rate> --batch-size <batch_size>

• dirpath: the dataset directory path
• outpath: a path to store the training history and the model weights
• epochs: the number of training epochs
• learning-rate: optimizer learning rate value
• batch-size: the number of instances to compose a mini-batch

Enroll

To enroll new dog breeds, run the following command:

python enroll.py <dirpath> <outpath> <modelpath> --epochs <num_epochs> --learning-rate <learning_rate> --batch-size <batch_size>

• dirpath: the dataset directory path
• outpath: a path to store the enroll history and the enrolled model weights
• modelpath: a path to the pre-trained dog breed recognition model
• epochs: the number of fine-tuning epochs
• learning-rate: optimizer learning rate value
• batch-size: the number of instances to compose a mini-batch

Eval

To evaluate the enrolled breeds, run the following command:

python eval.py <dirpath> <outpath> <modelpath> --batch-size <batch_size>

• dirpath: the dataset directory path
• outpath: a path to store the evaluation history
• modelpath: a path to the pre-trained dog breed recognition model
• batch-size: the number of instances to compose a mini-batch

Next steps

• Recognize the mutts using one of the above strategies:

  i. Set a confidence threshold for the predictions made by the model (i.e. 50% confidence)

  ii. Get the centroid from each class and use distance comparison. (i.e. instance have same dist + margin from 2+ centroids or instance is threshold farter then any centroid)

• Improve default model classification using a dog detector to improve dataset quality (i.e. YOLO)

• Improve enroll procedure with meta-learning fast adaptation methods using learn2learn (i.e. MAML)

• Implement web interface using Flask or Django

• Export model using proper PyTorch tools for web development (i.e. ONNX)

• Add TensorBoard

Contributions

I'm always opened to discussions and contributions. So, if you find interesting what I developed here and have any suggestion to enhance the codes, please, let me now in the issues.