All my YOLO-based models for various applications
- Overview
- Metallurgical Applications
- Counter-Strike 2 Object Detection
- Valorant Object Detection
- Deadlock Object Detection
- Overwatch 2 Object Detection
- Fortnite Object Detection
- How to Use the Models
- All Models
This repository showcases my YOLO models, customized for diverse applications, including industrial, gaming, and general object detection tasks. From metallurgical inspections to in-game object detection, these models leverage cutting-edge deep learning to achieve high precision.
- Model: YOLO11l
- Details: Optimized for detecting defects in fluorescent penetrant inspections.
- Access Model on Hugging Face
- Model: YOLO11x
- Details: Designed to identify welding defects with high accuracy.
- Access Model on Hugging Face
YOLO models tailored for detecting objects and events in Counter-Strike 2.
- YOLOv9c: Hugging Face Link
- YOLOv10s: Hugging Face Link
- YOLOv10m: Hugging Face Link
- YOLOv10b: Hugging Face Link
Models designed to detect in-game objects in Valorant.
- YOLOv10b: Hugging Face Link
- YOLO11m: Hugging Face Link
Custom YOLO models for detecting objects in Deadlock.
- YOLO11l: Hugging Face Link
Advanced object detection for Overwatch 2.
- YOLO11m: Hugging Face Link
Advanced object detection for Fortnite.
- YOLO11m: Hugging Face Link
Easily integrate the models into your projects using the ultralytics library.
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'your_model.pt')
# Run inference on an image
model.predict(
'image.png',
save=True,
device=0
) You can find all my models on Hugging Face: https://huggingface.co/jparedesDS
If you'd like to contribute, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License. See the LICENSE file for details.