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7ee2ab0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | PHASES = [
{"label": "Dev (test-dev2024)", "codename": "test-dev2024"},
{"label": "Standard (test-standard2024)", "codename": "test-standard2024"},
{"label": "Challenge (test-challenge2024)", "codename": "test-challenge2024"},
]
CHALLENGE_TYPES = ["Object Detection", "Instance Segmentation"]
LEADERBOARD_METRICS = ["bbox_mAP", "bbox_AP50", "segm_mAP", "segm_AP50"]
DEFAULT_SORT_METRIC = "segm_AP50"
LEADERBOARD_FILE = "leaderboard.jsonl"
CHALLENGE_PHASE = "test-challenge2024"
EVAL_DETAILS_MD = """
### How is the Score Calculated?
Your submission is evaluated automatically against hidden ground-truth annotations using **pycocotools**.
| Metric | Description |
|--------|-------------|
| `bbox_mAP` | Bounding box mean average precision |
| `bbox_AP50` | Bounding box AP at IoU = 0.50 |
| `segm_mAP` | Segmentation mean average precision |
| `segm_AP50` | Segmentation AP at IoU = 0.50 *(default ranking metric)* |
"""
FORMAT_MD = """
### Submission Format
Your JSON file must be a **list of annotation objects**, each containing:
```json
[
{
"image_id": 123,
"category_id": 101,
"score": 0.95,
"area": 1024.0,
"bbox": [x, y, width, height],
"segmentation": [[x1, y1, x2, y2, ...]]
},
...
]
```
"""
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