yolo_seg_hf

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.

You can load and use this model via the ultralytics Python library:

from ultralytics import YOLO

# Load the model
model = YOLO("path_to_model.pt")

# Perform inference on an image
results = model.predict("screenshot.jpg", conf=0.35, retina_masks=True)

# Extract segmentation masks and bounding boxes
for result in results:
    masks = result.masks
    boxes = result.boxes