Half-Life Player Segmentation Model
This is a YOLO-based segmentation model trained specifically to detect and segment player models in Half-Life. It is designed for real-time computer vision applications, including automated tracking and aim assistance research.
Model Details
Architecture: YOLO Segmentation (Ultralytics)
Task: Object Detection & Instance Segmentation
Target: In-game player characters
Dataset: Custom dataset of Half-Life gameplay imagery, featuring varied environments and player skins.
1from ultralytics import YOLO
2
3# Load the model
4model = YOLO("path_to_model.pt")
5
6# Perform inference on an image
7results = model.predict("screenshot.jpg", conf=0.35, retina_masks=True)
8
9# Extract segmentation masks and bounding boxes
10for result in results:
11 masks = result.masks
12 boxes = result.boxes
