archaia/dataset_v1_artifacts_nobg

Artifact Images Without Background

This dataset contains images of archaeological artifacts with the background removed.
The images were generated by filtering and processing the original dataset:

https://huggingface.co/datasets/archaia/dataset_v1

The following sections describe the processing pipeline used to obtain the final dataset, including:

1. Image filtering using CLIP
2. Object detection and background removal

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Processing Pipeline

1. Image Filtering with CLIP

The first step filters the images from the original dataset to retain only those containing archaeological artifacts.

The filtering process is implemented in ComputeCls.py.

Execution

Run the following command:

python ComputeCls.py --input_path [path_to_input_dir]

Output

The script performs the following operations:

• Classifies images using CLIP
• Generates a JSON file containing classification results
• Moves images classified as artifacts to a new directory
• Saves visualization plots of images identified as artifacts
• This step produces a filtered subset containing only artifact-related images.

2. Object Detection and Background Removal

The second stage detects artifacts in the images and removes their backgrounds.

The code is implemented in test_finetuned_removebg.py.

Execution

Run the following command:

python test_finetuned_removebg.py --input_path [path_to_input_dir] --output_path [path_to_output_dir]

Processing Steps

• The script iterates over each image in the input directory.
• An object detection model detects potential artifacts in the image.
• The detection model is a fine-tuned DETR model trained on 303 labeled images. Details about the training data and the model checkpoint will be provided in a separate repository.
• Only detections labeled as "Archaeological artifacts" are retained.
• The detected bounding boxes are used to crop the original images.
• Since a single image may contain multiple artifacts, each detection is processed independently.
• For every cropped artifact, the BRIAAI background removal model is applied to remove the background.

Output

• The processed images are saved in the specified output directory.

• If multiple artifacts are detected in the same image, each artifact is saved separately using the following naming convention: original_filename_[detection_id].png

  • Example:
    • 000000029_0.png
    • 000000029_1.png
    • 000000029_2.png

• Each output image contains a single archaeological artifact with the background removed.

Result

The final dataset consists of:

• Images containing individual archaeological artifacts
• Background removed
• Artifacts automatically extracted from the original dataset using a pipeline based on:
  • CLIP image filtering
  • DETR object detection
  • BRIAAI background removal