| --- |
| 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 |
|
|
| ```python |
| 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 |
|
|
