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, ...]]
  },
  ...
]
```
"""