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frendyrachman/
mlbb-ai-clipper

Name: mlbb-ai-clipper
Availability: InStock

apache-2.0

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YOLO Custom Model for Object Detection

This repository contains a specially trained YOLO model designed for the epic moment UI detection task in the mobile legend (MLBB) video game. The model was trained using a public dataset without including personal information. The pre-trained model is YOLOv8m using a 6 Core 12 thread CPU device and NVIDIA RTX 3090 24GB GDDR6X.

Model Details

Architecture: YOLO
Framework: PyTorch
Performance: mAP@50, precision, recall, etc.
Model File: best.pt

Getting Started

Installation

Ensure you have the following dependencies installed:

pip install ultralytics

Usage

To use the model for inference, follow these steps:

python

from ultralytics import YOLO

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

# Perform inference on an image
results = model("path/to/your/image.jpg")

# Display or save results
results.show()  # Show predictions
results.save("output/")  # Save predictions to output folder

Input Format

Image: Any standard image format (e.g., JPG, PNG).
Video: Supported for object tracking and frame-by-frame inference.

Output

The model outputs bounding boxes, class labels, and confidence scores for each detected object.

Example Results

Here are some example results generated by the model:

Input Image	Output Image
Input	Output

Training Details

Pretrained Weights: YOLO pretrained weights were used as the starting point.
Hyperparameters: Custom hyperparameters for learning rate, epochs, and batch size were used. Refer to the config.yaml file for details.
Hardware: Training was conducted on NVIDIA RTX 3090.

Demo

You can interact with this model through a hosted Gradio app:
Gradio Demo (if available).

Limitations

The model is optimized for the specific dataset it was trained on. It may not generalize well to other datasets.
Objects not present in the training dataset may not be detected accurately.

License

This model is licensed under the MIT License. Feel free to use it for personal or commercial purposes with attribution.

Acknowledgments

Built using Ultralytics YOLO.
The dataset used for model training is taken from public datasets such as public streams or clips without including any personal information.

Contact

For questions, issues, or suggestions, feel free to reach out:

Author: Frendy Rachman (https://huggingface.co/frendyrachman)
Email: frendyrachman@gmail.com

frendyrachman/mlbb-ai-clipper