Yolov5n Csgo

🚀 keremberke/yolov5n-csgo

A YOLOv5-based object detection model for CSGO scenarios, offering high precision in detecting objects.

🚀 Quick Start

This section provides a guide on how to install the yolov5 library, load the model, perform predictions, and fine-tune the model on a custom dataset.

📦 Installation

Install the yolov5 library using the following command:

pip install -U yolov5

💻 Usage Examples

Basic Usage

Load the model and perform a basic prediction:

import yolov5

# load model
model = yolov5.load('keremberke/yolov5n-csgo')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

Advanced Usage

Fine-tune the model on your custom dataset:

yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-csgo --epochs 10

📚 Documentation

Model Information

Model Performance

The model keremberke/yolov5n-csgo was evaluated on the keremberke/csgo-object-detection dataset's validation split. The following metric was recorded:

Visual Example

More Models

More models are available at: awesome-yolov5-models