Yolov5s Csgo

🚀 keremberke/yolov5s-csgo

A YOLOv5-based object detection model for CSGO scenarios, providing high-precision object detection capabilities.

📦 Installation

• Install yolov5:

pip install -U yolov5

💻 Usage Examples

Basic Usage

import yolov5

# load model
model = yolov5.load('keremberke/yolov5s-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

# Finetune the model on your custom dataset
yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5s-csgo --epochs 10

📚 Documentation

Model Index

• Name: keremberke/yolov5s-csgo
• Results:
  • Task:
    • Type: object-detection
  • Dataset:
    • Type: keremberke/csgo-object-detection
    • Name: keremberke/csgo-object-detection
    • Split: validation
  • Metrics:
    • Type: precision (since mAP@0.5 is not available on hf.co/metrics)
    • Value: 0.9244741806408356 (min: 0.0 - max: 1.0)
    • Name: mAP@0.5

More models available at: awesome-yolov5-models