| ---
|
| license: mit
|
| tags:
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| - ultralytics
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| - yolo11
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| - object-detection
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| - wildlife
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| - bird
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| library_name: ultralytics
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| base_model: ultralytics/yolo11n
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| datasets:
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| - synthet/image-scoring-model
|
| ---
|
|
|
| # bird-detect-v0
|
|
|
| YOLO11n detect model fine-tuned on CUB-200-2011 bird bounding boxes (single class `bird`).
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| Companion to [synthet/eye-pose-v0](https://huggingface.co/synthet/eye-pose-v0) for subject localization
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| when eye keypoints are not required (species crops, gating, counting).
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|
|
| Used with the [image-scoring-model](https://github.com/synthet/image-scoring-model) `eye-quality detect` CLI.
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|
|
| ## Class
|
|
|
| | Index | Name |
|
| |------:|------|
|
| | 0 | bird |
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|
|
| ## Training
|
|
|
| - **Base:** YOLO11n (`yolo11n.pt`)
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| - **Dataset:** CUB-200-2011 boxes via `data/wildlife_bird_det` (~10k train / 1.7k val)
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| - **Epochs:** 100 (imgsz 640, batch 16)
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| - **Final validation (epoch 100):**
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| - Box mAP50: **0.994**
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| - Box mAP50-95: **0.892**
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| - Precision: **0.993**
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| - Recall: **0.997**
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|
|
| ## Usage
|
|
|
| ```python
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| from ultralytics import YOLO
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|
|
| model = YOLO("hf://synthet/bird-detect-v0/bird_detect_v0.pt")
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| results = model.predict("bird.jpg", imgsz=640)
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| ```
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|
|
| Or with the `eye_quality` package:
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|
|
| ```bash
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| pip install -e "git+https://github.com/synthet/image-scoring-model.git"
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| huggingface-cli download synthet/bird-detect-v0 bird_detect_v0.pt --local-dir models/
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| python -m eye_quality detect bird.jpg --weights models/bird_detect_v0.pt
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| ```
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|
|
| ## Limitations
|
|
|
| - Single-class bird boxes only; not a multi-species detector.
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| - Trained on CUB-200 studio/Flickr-style photos; validate on your field library.
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| - CUB labels are typically one bird per image; crowded frames need care.
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|
|
