Instructions to use beanyzoldyck/tiktok-watermark-yolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use beanyzoldyck/tiktok-watermark-yolo with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("beanyzoldyck/tiktok-watermark-yolo", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
TikTok Watermark Detection β YOLOv11
A fine-tuned YOLOv11n model for detecting TikTok watermarks in video frames and images.
Model Details
- Base model: YOLOv11n (
yolo11n.pt) - Task: Object detection (single class β TikTok watermark)
- Input size: 640Γ640
- Format: PyTorch (.pt)
Usage
from ultralytics import YOLO
model = YOLO("beanyzoldyck/tiktok-watermark-yolo")
results = model.predict("video.mp4", conf=0.10)
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist()
conf = box.conf[0].item()
print(f"Watermark detected at ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}) conf={conf:.4f}")
Training
Trained with Ultralytics YOLO on a curated TikTok watermark dataset using yolo11n as the base checkpoint. Default training configuration: 640px image size.
Intended Use
Detecting TikTok watermarks for content moderation, video processing pipelines, and research purposes.
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