# yolov5_dection **Repository Path**: dong19960127/yolov5_dection ## Basic Information - **Project Name**: yolov5_dection - **Description**: 基于yolov5模型+pyqt，实现图片和文件夹内图片的目标检测识别，可用于自动驾驶、无人机和机器人生产应用。 - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2025-06-23 - **Last Updated**: 2025-06-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

English | [简体中文](.github/README_cn.md)
YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

To request a commercial license please complete the form at Ultralytics Licensing.

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Segmentation ⭐ NEW
Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
Classification Usage Examples  Open In Colab ### Train YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. ```bash # Single-GPU python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 # Multi-GPU DDP python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 ``` ### Val Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: ```bash bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ``` ### Predict Use pretrained YOLOv5s-cls.pt to predict bus.jpg: ```bash python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg ``` ```python model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub ``` ### Export Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT: ```bash python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 ```
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