🚀 YOLOv5 空中绵羊目标检测模型

本项目基于 YOLOv5 实现空中绵羊的目标检测，能高效准确地识别图像中的绵羊目标，在相关视觉领域具有重要应用价值。

🚀 快速开始

安装依赖

安装 yolov5：

pip install -U yolov5

模型使用

基础用法

加载模型并进行预测：

import yolov5

# load model
model = yolov5.load('keremberke/yolov5n-aerial-sheep')
  
# 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/')

高级用法

在自定义数据集上微调模型：

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

📚 详细文档

更多可用模型请参考：awesome-yolov5-models

🔧 技术细节