chocochip119/wardy-m05-hazard-detector

Wardy M05 Hazard Detector

A YOLO11n-based object detection model trained to detect potentially hazardous objects in indoor environments.

Classes

ID	Class
0	scissors
1	knife
2	cutter
3	syringe

Model Details

• Architecture: Ultralytics YOLO11n
• Task: Object detection
• Input size: 640 x 640
• Weight format: PyTorch .pt
• Dataset: chocochip119/hazard
• Recommended weight: weights/best.pt

Versions

Revision	Training epochs	Best mAP50	Best mAP50-95
hazard-objects-v1-full-v1	100	0.8681	0.6555
hazard-objects-v2-finetune-v1	11	0.7721	0.5359
hazard-objects-v2-finetune-v1-extend20	20	0.8260	0.6409
hazard-objects-v2-finetune-v2	30	0.8322	0.6388
hazard-objects-v2-finetune-v2-extend20	8	0.8211	0.6202
hazard-objects-v2-finetune-v3	40	0.8381	0.6113
hazard-objects-c270-finetune-v1	20	0.8348	0.6382

The metrics above are taken from the epoch with the highest validation mAP50-95 in each run. Direct comparison requires evaluation on the same fixed test set.

Usage

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weight_path = hf_hub_download(
    repo_id="chocochip119/wardy-m05-hazard-detector",
    filename="weights/best.pt",
    revision="main",
)

model = YOLO(weight_path)
results = model.predict(
    source="example.jpg",
    imgsz=640,
    conf=0.25,
    save=True,
)

• To load a specific release, replace main with a version tag such as hazard-objects-v2-finetune-v2.
• Limitations
• The model may produce false positives or miss hazardous objects.
• Performance may decrease under poor lighting, occlusion, unusual camera angles, or unfamiliar environments.
• The output should not be used as the sole basis for safety-critical decisions.
• Additional validation and human review are required before real-world deployment.

License

A distribution license has not yet been specified. Verify the licenses and usage conditions of the model, base model, and training data before use or redistribution.