SnakeAid Detect YOLOv12 v4 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 | SnakeTraining_V4_YOLOv12_Khiem_Bbox5000_20251213_1828.pt |
| Size | 37.96 MB |
| SHA256 | b215c030b8f482dcaeed15e4838ad9e6d4c474beefbd330cc5e45cc4c893872b |
| Dataset | the-khiem7/snakeaid-yolov12-5000-bbox |
| Architecture | YOLOv12 |
| Version | v4 |
| Trained by | Khiem |
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-yolov12-v4-5000bbox",
filename="SnakeTraining_V4_YOLOv12_Khiem_Bbox5000_20251213_1828.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-yolov12-v4-5000bbox",
filename="SnakeTraining_V4_YOLOv12_Khiem_Bbox5000_20251213_1828.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: YOLOv12
- Version: v4
- Trained by: Khiem
The original training code, hyperparameters, random seed, and evaluation logs were not included in this upload.
Related Dataset Version
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