SnakeAid Detect YOLOv12s 17e 5000BBox
Model Summary
This repository contains a SnakeAid Detect YOLOv12 checkpoint for snake object detection. The checkpoint is published as a .pt file and linked to the dataset version used for the corresponding experiment.
Safety note: this model is intended for research, prototyping, and application experiments. Do not use it as the sole authority for snake identification, emergency response, or animal handling decisions.
Checkpoint
| Field | Value |
|---|---|
| File | SnakeAid_SnakeDetector_YOLOv12s_5000Bbox_17epoch_Nhan.pt |
| Size | 17.78 MB |
| SHA256 | 230e2db84bbcd3f07951ab8d5ed2be271a951f66655d281bf760ea5b9afdcaf0 |
| Dataset | the-khiem7/snakeaid-yolov12-5000-bbox |
| Architecture | YOLOv12s |
| Epochs | 17 |
| Trained by | Nhan |
Intended Use
- Snake detection experiments with YOLOv12-compatible tooling.
- Comparing SnakeAid dataset versions and checkpoint variants.
- Downstream evaluation, prototyping, and transfer-learning baselines.
Out-of-Scope Use
- Safety-critical snake identification without human/expert review.
- Medical, veterinary, emergency, or wildlife handling decisions based only on model output.
- Deployment to image domains not represented by the linked dataset without additional validation.
Dataset
This model is linked to the-khiem7/snakeaid-yolov12-5000-bbox.
The Hub metadata also includes this dataset in the datasets field so Hugging Face can display the training-data relationship.
Usage Example
from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(
repo_id="the-khiem7/snakeaid-detect-yolov12s-17e-5000bbox",
filename="SnakeAid_SnakeDetector_YOLOv12s_5000Bbox_17epoch_Nhan.pt",
)
print(checkpoint_path)
If your environment supports Ultralytics-compatible YOLOv12 checkpoints:
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
checkpoint_path = hf_hub_download(
repo_id="the-khiem7/snakeaid-detect-yolov12s-17e-5000bbox",
filename="SnakeAid_SnakeDetector_YOLOv12s_5000Bbox_17epoch_Nhan.pt",
)
model = YOLO(checkpoint_path)
results = model.predict("path/to/image.jpg")
Evaluation
No standardized metrics were uploaded with this checkpoint. Evaluate it on a held-out split or a domain-specific benchmark before comparing it with other detectors or deploying it.
Training And Reproducibility Notes
Known metadata:
- Architecture: YOLOv12s
- Epochs: 17
- Trained by: Nhan
The original training code, hyperparameters, random seed, and evaluation logs were not included in this upload.
Related Dataset Version
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