Cass Detect

Introduction

This repository contains code for a self-training machine learning pipeline developed to detect cassowaries or other animals on roads and roadsides.

Contact: kumli, msha3421, Hommus

Installation

Requirements:

• Docker
• A GPU with at least 16GB of memory

Clone the repository, then build and run the docker image

docker buildx build -t laarma .
docker run --gpus all -it -v $PWD:/app laarma:latest

Prepare Data

• Download a sample cassowary dataset (608MB) from here
• Organise the file structure as follows:

  data                                    sample_dataset
      |- stage1                               |
          |- background
              |- images (*.jpeg)      <----   |-  background_images (*.jpeg)
          |- class_images (*.jpeg)    <----   |-  web_images (*.jpeg)
      |- stage2
          |- raw_data (*.jpeg)        <----   |-  field_images (*.jpeg)
  trainer
      |- validation
          |- images (*.jpeg)          <----   |-  validation_images (*.jpeg)
          |- labels (*.txt)           <----   |-  validation_labels (*.txt)

• Note:
  • The sample dataset is intended for illustrative purposes.
  • The sample dataset has been de-identified to address privacy concerns.
  • The performance of a model trained using the sample dataset may not match the results reported in the project report or associated papers.

Run Example

1. Data Synthesis

Synthesise training data by merging images of cassowaries with various environmental backgrounds. This will improve the model's ability to recognise cassowaries in different settings.

python src/stage1.py

Train the object detector for use in the field.

python src/train.py --stage 1

2. Autolabel Field Data

After receiving new data from the field, we have placed it in the data/stage2/raw_data folder. Run the following script to autolabel it.

python src/stage2.py

Train the object detector again for improved performance in the field.

python src/train.py --stage 2

3. Field Data Augmentation

Adopt a similar strategy to stage 1 and synthesise data from the labelled data.

python src/stage3.py

After stage 3, it's recommended to rerun the training script to integrate the synthesised data, improving the overall model performance.

python src/train.py --stage 3

Bring Your Own Data

• Step 1. Place images with your class of interest in data/stage1/class_images
• Step 2. Place images without your class of interest in data/stage1/background/images
• Step 3. Follow the steps from the example

Run Evaluation

To evaluate the model's performance after training, you can run the evaluation using the following command. This step uses the best model weights obtained from the previous training stages and evaluates it against a predefined validation dataset to measure its accuracy, precision, and other relevant metrics.

Run the following command in your docker container:

python src/train.py --action eval