基于yolov8的水果蔬菜检测系统python源码+onnx模型+评估指标曲线+精美GUI界面

作品简介

【测试环境】

windows10

anaconda3+python3.8

torch==2.3.0

ultralytics==8.3.79

【模型可以检测出94类别】

Bitter melon,Brinjal,Cabbage,Calabash,Capsicum,Cauliflower,Cluster bean,Curry Leaf,Garlic,Ginger,Green Chili,Green Peas,Healthy,Hyacinth Beans,Lady finger,Onion,Potato,Sapodilla,Sponge Gourd,Tomato,apple,banana,beans,beef,beet,beetroot,bellpepper,bittergourd,blueberries,bottle,bottlegourd,bread,brinjal,broccoli,butter,cabbage,capsicum,carrot,cat-Abyssinian,cat-Bengal,cat-Birman,cauliflower,charger,cheese,chicken,chickenbreast,chilli,chocolate,corn,cucumber,egg,eggplant,eggs,fig,flour,garlic,ginger,goat_cheese,grape,green_beans,ground_beef,haft-cabbage,half carrot,half onion,ham,heavy_cream,jalapeno,kiwi,lemon,lettuce,lime,milk,mushrooms,onion,orange,papaya,pear,pineapple,pomogranate,potato,pumpkin,raddish,redonion,scarletgourds,shrimp,spinach,spounggourd,strawberries,strawberry,sugar,sweet_potato,tomato,watermelon,zucchini

【使用步骤】

使用步骤:

(1)首先根据官方框架安装好yolov8环境,并安装好pyqt5

(2)切换到自己安装的yolov8环境后,并切换到源码目录,执行python main.py即可运行启动界面,进行相应的操作即可

【提供文件】

python源码

yolov8n.onnx模型(不提供pytorch模型)

训练的map,P,R曲线图(在weights\results.png)

测试图片(在test_img文件夹下面)

注意不提供数据集

创作时间: