farazv2/overlay-model-yolo

YOLO Overlay Detection Model

This model was trained to detect and segment overlay elements in images/videos using YOLOv8 segmentation.

This repository contains two primary model files:

• best.pt: The model checkpoint with the best validation metrics seen so far.
• last.pt: The final checkpoint from the most recent training run, used for resuming.

Model Details

• Model Type: YOLOv8 Instance Segmentation
• Architecture: yolov8m-seg
• Framework: Ultralytics YOLO
• Training Date: 2025-11-04
• Task: Instance Segmentation
• Classes: Overlay elements

Performance Metrics (from last 'best.pt')

Metric	Value
Box [email protected]	0.9093
Box [email protected]:0.95	0.7576
Mask [email protected]	0.6030
Mask [email protected]:0.95	0.2714

Usage

Installation

pip install ultralytics

Inference (Using the best model)

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download best model
model_path = hf_hub_download(
    repo_id="farazv2/overlay-model-yolo",
    filename="best.pt"
)

# Load model
model = YOLO(model_path)

# Run inference
results = model('image.jpg')

Resuming Training

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download last model
model_path = hf_hub_download(
    repo_id="farazv2/overlay-model-yolo",
    filename="last.pt"
)

# Load model and resume
model = YOLO(model_path)
model.train(data='path/to/data.yaml', resume=True)

Training Configuration

Parameter	Value
Epochs	10 (per run)
Image Size	640
Optimizer	AdamW
Initial Learning Rate	0.001
Batch Size	24
Mixed Precision	True
Patience	20

License

This model is released under the AGPL-3.0 license, following Ultralytics YOLOv8 licensing.