auto_data_labeling

Automated segmentation and bounding box labeling pipeline using SAM3. This automatically generates normalized YOLO-pose keypoint data for underwater gate detection.

Prerequisites

1. Compute Environment: This pipeline supports CUDA, MPS, and CPU, but running it on a GPU cluster is highly recommended for optimal inferencing speeds

2. Hugging Face Authentication: You will need a valid Hugging Face access token to download the model weights. Run the following command in your terminal and follow the prompts to paste your token:

huggingface-cli login

Installation

1. Install uv once per machine:

curl -LsSf https://astral.sh/uv/install.sh | sh

(or brew install uv on macOS).

2. Clone this auto-labeling repository and navigate into it:

git clone https://github.com/berkeleyauv/auto_data_labeling.git
cd auto_data_labeling

3. Create the virtual environment pinning Python 3.11, which creates a .venv/ directory:

uv venv --python 3.11
source .venv/bin/activate

4. Install the necessary libraries from the requirements file:

# Install pytorch with CUDA support
uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

# Install the other packages
uv pip install -r requirements.txt

Data Preparation

1. Inside your cloned auto_data_labeling repository, create a directory to hold your raw images:

mkdir -p data/raw_images

2. Place all the raw dataset images you want to annotate (must be .png, .jpg, or .jpeg) into this folder.

Inferencing

Submit the inference script to the Slurm scheduler:

sbatch submit_inference.sh

• --input_dir is where to pull the images from
• --output_dir is where to save the raw_predictions.json file to

To see live outputs and track inference progress:

# Check your job ID
squeue -u $USER

# Replace 123456 with your actual job ID
tail -f slurm-123456.out

Human Review

1. Launch the Gradio UI

bash launch_QA.sh

• --json_path is where the generated predictions are
• --image_dir is where the raw images are for rendering

2. Review the annotations:

• Click the public gradio.live link generated in your terminal to open the UI in your web browser.
• Review the predicted corners (cyan dots) on the underwater gate frame.
• If a corner is incorrect, select the corresponding radio button (e.g., TL for Top-Left) and click on the image to manually move the point.
• Click Accept & Export to save the frame and move to the next image.
• When finished, a completion screen will appear.

Results

• A ./data/labels directory will have been created
• Each image has a corresponding .txt file containing the normalized keypoints and bounding boxes of the gate