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keremberke/yolov5m-csgo

sourceHugging Faceupdated 4y agoView on Hugging Face

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tags:

—yolov5
—yolo
—vision
—object-detection
—pytorch libraryname: yolov5 libraryversion: 7.0.6 inference: false

datasets:

—keremberke/csgo-object-detection

model-index:

—name: keremberke/yolov5m-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: 0.9318950805677579 # min: 0.0 - max: 1.0 name: mAP@0.5 ---

How to use

—Install yolov5:

bash

pip install -U yolov5

—Load model and perform prediction:

python

import yolov5

# load model
model = yolov5.load('keremberke/yolov5m-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/')

—Finetune the model on your custom dataset:

bash

yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-csgo --epochs 10

More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)