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WOW iNat Annotated - Animal Detection Dataset
Dataset de detection d'animaux base sur Horama/WOW_iNat_augmented, annote automatiquement avec Grounding DINO pour la detection de bounding boxes.
Source
- Dataset source:
Horama/WOW_iNat_augmented - Detection:
IDEA-Research/grounding-dino-base(Grounding DINO) - Double-check:
openai/clip-vit-large-patch14(CLIP)
Statistiques
| Metrique | Valeur |
|---|---|
| Images totales | 4,695 |
| Images avec animal | 4,645 (98.9%) |
| Images sans animal | 50 (1.1%) |
Detail par split
| Split | Total | Avec animal | Sans animal |
|---|---|---|---|
test_prod |
3,334 | 3,316 | 18 |
test_gen |
1,361 | 1,329 | 32 |
Splits
- test_prod
- test_gen
Colonnes
| Colonne | Type | Description |
|---|---|---|
image |
Image | L'image PIL |
is_animal |
bool | True si au moins un animal detecte |
has_animal |
bool | Alias de is_animal |
decision |
str | kept_dino, kept_clip, removed_clip, removed_dino |
num_detections |
int | Nombre de bounding boxes |
objects |
dict | Bounding boxes, confidences, labels |
clip_score |
float | Score CLIP animal (null si non verifie) |
Format des objets
example["objects"] = {
"bbox": [[x_min, y_min, x_max, y_max], ...],
"confidence": [0.85, ...],
"label": ["animal", ...],
}
Utilisation
from datasets import load_dataset
ds_test_prod = load_dataset("Horama/WOW_iNat_annoted", split="test_prod")
ds_test_gen = load_dataset("Horama/WOW_iNat_annoted", split="test_gen")
# Filtrer
animals_only = ds_train.filter(lambda x: x['is_animal'])
Cree par Horama
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