lit-ecology-classifier 0.1

Image Classifier optimised for ecology use-cases

# Lit Ecology Classifier

Lit Ecology Classifier is a machine learning project designed for image classification tasks. It leverages PyTorch Lightning for streamlined training and evaluation processes.

## Features

• Easy configuration and setup

• Utilizes PyTorch Lightning for robust training and evaluation

• Supports training on multiple GPUs

• Test Time Augmentation (TTA) for enhanced evaluation

• Integration with Weights and Biases for experiment tracking

## Installation

To install Lit Ecology Classifier, use pip:

`bash pip install lit-ecology-classifier `

## Usage

### Training

To train the model, use the following command:

`bash python -m lit_ecology_classifier.main --max_epochs 20 --dataset phyto --priority config/priority.json `

### Inference

To run inference on unlabelled data, use the following command:

`bash python -m lit_ecology_classifier.predict --datapath /path/to/data.tar --model_path /path/to/model.ckpt --outpath ./predictions/ `

## Configuration

The project uses an argument parser for configuration. Here are some of the key arguments:

### Training Arguments

• –datapath: Path to the tar file containing the training data.

• –train_outpath: Output path for training artifacts.

• –main_param_path: Main directory where the training parameters are saved.

• –dataset: Name of the dataset.

• –use_wandb: Use Weights and Biases for logging.

• –priority_classes: Path to the JSON file with priority classes.

• –balance_classes: Balance the classes for training.

• –batch_size: Batch size for training.

• –max_epochs: Number of epochs to train.

• –lr: Learning rate for training.

• –lr_factor: Learning rate factor for training of full body.

• –no_gpu: Use no GPU for training.

### Inference Arguments

• –outpath: Directory where predictions are saved.

• –model_path: Path to the model file.

• –datapath: Path to the tar file containing the data to classify.

• –no_gpu: Use no GPU for inference.

• –no_TTA: Disable test-time augmentation.

## Documentation

Detailed documentation for this project is available at [Read the Docs](https://lit-ecology-classifier.readthedocs.io).

### Example SLURM Job Submission Script

Here is an example SLURM job submission script for training on multiple GPUs:

`bash #!/bin/bash #SBATCH --account="em09" #SBATCH --constraint='gpu' #SBATCH --nodes=2 #SBATCH --ntasks-per-core=1 #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task=12 #SBATCH --partition=normal #SBATCH --constraint=gpu #SBATCH --hint=nomultithread #SBATCH --output=slurm/slurm_%j.out #SBATCH --error=slurm/slurm_%j.err export OMP_NUM_THREADS=12 #$SLURM_CPUS_PER_TASK cd ${SCRATCH}/lit_ecology_classifier module purge module load daint-gpu cray-python source lit_ecology/bin/activate python -m lit_ecology_classifier.main --max_epochs 2 --dataset phyto --priority config/priority.json `