Examples

<div align="center"> <img width="416" alt="turhancan97/yolov5-detect-trash-classification" src="https://huggingface.co/turhancan97/yolov5-detect-trash-classification/resolve/main/example1.jpg"> </div> <div align="center"> <img width="416" alt="turhancan97/yolov5-detect-trash-classification" src="https://huggingface.co/turhancan97/yolov5-detect-trash-classification/resolve/main/example2.jpg"> </div> <div align="center"> <img width="416" alt="turhancan97/yolov5-detect-trash-classification" src="https://huggingface.co/turhancan97/yolov5-detect-trash-classification/resolve/main/example3.jpg"> </div>

How to use

• Install yolov5:

pip install -U yolov5

• Load model and perform prediction:

import yolov5

# load model
model = yolov5.load('turhancan97/yolov5-detect-trash-classification')
  
# 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=416)

# 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:

yolov5 train --data data.yaml --img 416 --batch 16 --weights turhancan97/yolov5-detect-trash-classification --epochs 10