farazv2/latest-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: 2025-11-06
• Task: Instance Segmentation
• Classes: Overlay elements

Performance Metrics (from last 'best.pt')

Metric	Value
Box [email protected]	0.9261
Box [email protected]:0.95	0.8106
Mask [email protected]	0.6172
Mask [email protected]:0.95	0.2904

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/latest-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/latest-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	2 (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.