README.md

metadata

license: mit
task_categories:
  - object-detection
  - image-classification
tags:
  - animal-detection
  - animal-reidentification
  - bounding-box
  - face-detection
  - body-detection
  - tracklets
  - re-identification
size_categories:
  - 1K<n<10K
configs:
  - config_name: face_and_body
    data_files:
      - split: test
        path: face_and_body/test-*
  - config_name: body_only
    data_files:
      - split: test
        path: body_only/test-*
  - config_name: original_with_face_body_bbox
    data_files:
      - split: test
        path: original_with_face_body_bbox/test-*
  - config_name: original_with_body_bbox
    data_files:
      - split: test
        path: original_with_body_bbox/test-*
  - config_name: face_tracklets
    data_files:
      - split: test
        path: face_tracklets/test-*
  - config_name: body_tracklets
    data_files:
      - split: test
        path: body_tracklets/test-*

Zoo Animal Re-Identification Dataset

A dataset for animal re-identification with 2,705 body images, 1,192 face crops, and 6 configurations.

Configurations

1. face_and_body

Individual frames with both face and body crops.

Features:

• date: Date of capture (YYYY-MM-DD)
• time: Time of capture (HH:MM:SS)
• class: Animal name
• video: Source video filename
• frame_number: Frame number in video
• camera: Camera ID
• face_image: Cropped face image
• body_image: Cropped body/full image

2. body_only

Individual frames with body crops only.

Features:

• date, time, class, video, frame_number, camera: Same as above
• body_image: Cropped body image

3. original_with_face_body_bbox

Full original frames.

Features:

• date, time, class, video, frame_number, camera: Same as above
• image: Original full image

4. original_with_body_bbox

Full original frames.

Features:

• date, time, class, video, frame_number, camera: Same as above
• image: Original full image

5. face_tracklets

Face images grouped by tracklet (animal + video). Each row contains all face crops from one animal in one video, ordered by frame number.

Features:

• class: Animal name
• video: Source video filename
• camera: Camera ID
• frame_numbers: List of frame numbers (ordered)
• face_images: Sequence of face images (ordered by frame)

6. body_tracklets

Body images grouped by tracklet (animal + video). Each row contains all body crops from one animal in one video, ordered by frame number.

Features:

• class: Animal name
• video: Source video filename
• camera: Camera ID
• frame_numbers: List of frame numbers (ordered)
• body_images: Sequence of body images (ordered by frame)

Usage

from datasets import load_dataset

# Load individual frames with face and body
ds = load_dataset("Maxscha/test", "face_and_body", split="test")
print(ds[0]["face_image"])  # PIL Image
print(ds[0]["class"])  # Animal name

# Load face tracklets
ds = load_dataset("Maxscha/test", "face_tracklets", split="test")
print(len(ds[0]["face_images"]))  # Number of faces in this tracklet
print(ds[0]["class"])  # Animal name for this tracklet

# Load body tracklets
ds = load_dataset("Maxscha/test", "body_tracklets", split="test")
for img in ds[0]["body_images"]:
    print(img)  # Each PIL Image in the tracklet sequence

Animals

The dataset contains images of 5 animals:

• Sango
• Tilla
• M'Penzi
• Bibi
• Djambala

License

MIT