首页分享基于yolov5的海棠花花朵识别检测系统源码+数据集+实验报告.zip

基于yolov5的海棠花花朵识别检测系统源码+数据集+实验报告.zip

来源：花匠小妙招时间：2024-11-26 09:17

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Quick Start Examples

Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txtInference

Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.

import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc.Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ python detect.py --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streamTraining

Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).

$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16 Tutorials Train Custom Data ð RECOMMENDED Tips for Best Training Results âï¸ RECOMMENDED Weights & Biases Logging ð NEW Roboflow for Datasets, Labeling, and Active Learning ð NEW Multi-GPU Training PyTorch Hub â NEW TFLite, ONNX, CoreML, TensorRT Export ð Test-Time Augmentation (TTA) Model Ensembling Model Pruning/Sparsity Hyperparameter Evolution Transfer Learning with Frozen Layers â NEW TensorRT Deployment