测试开发环境

去官网下载yolov7的权重文件，放入weights目录下，运行detec.py文件测试是否安装成功，官网地址：权重下载

python detect.py --weights weights/yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

使用自己的VOC格式数据集训练

修改配置文件yolov7.yaml

首先修改cfg下training下的yolov7.yaml文件。

修改修改nc并设置为自己训练的类别个数

修改配置文件voc.yaml

关键点在于，这得是一个文件夹的路径，不能是txt文件的路径。。。。。。
如果设置txt文件的路径，很容易报BUG：

assertionerror:no labels found in //*/JPEGImages.cache can not train without labels

这个BUG搞了我很久
因为v7结构和原始的v5一样，v5官方就是给的文件夹：

v5的coco.yaml文件如下

# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../coco/images/train2017/
val: ../coco/images/train2017/

# number of classes
nc: 80

# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 
        'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 
        'teddy bear', 'hair drier', 'toothbrush']

可以看出，yaml文件中的训练集和验证集数据地址都是为一个装图像数据的文件夹（…/coco/images/train2017/ …/coco/images/train2017/），而不是我之前使用VOC2yolo代码转换生成的train.txt和val.txt。

VOC格式数据集转换COCO格式

官方代码是COCO格式的，就涉及到VOC数据集格式转COCO格式问题。
(这个.sh代码是项目找不到数据集的时候，就会调用他下载COCO…一定要把他注释掉。。。)

voc2yolov7.py代码：

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile

# 根据自己的数据标签修改
classes=["person"]


def clear_hidden_files(path):
    dir_list = os.listdir(path)
    for i in dir_list:
        abspath = os.path.join(os.path.abspath(path), i)
        if os.path.isfile(abspath):
            if i.startswith("._"):
                os.remove(abspath)
        else:
            clear_hidden_files(abspath)

def convert(size, box):
    dw = 1./size[0]
    dh = 1./size[1]
    x = (box[0] + box[1])/2.0
    y = (box[2] + box[3])/2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

def convert_annotation(image_id):
    in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
    out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    in_file.close()
    out_file.close()

wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, "VOCdevkit/")
if not os.path.isdir(data_base_dir):
    os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
if not os.path.isdir(work_sapce_dir):
    os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
if not os.path.isdir(annotation_dir):
        os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
if not os.path.isdir(image_dir):
        os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):
        os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, "images/")
if not os.path.isdir(yolov5_images_dir):
        os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
if not os.path.isdir(yolov5_labels_dir):
        os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
if not os.path.isdir(yolov5_images_train_dir):
        os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
if not os.path.isdir(yolov5_images_test_dir):
        os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
if not os.path.isdir(yolov5_labels_train_dir):
        os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
if not os.path.isdir(yolov5_labels_test_dir):
        os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)

train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
list_imgs = os.listdir(image_dir) # list image files
probo = random.randint(1, 100)
print("Probobility: %d" % probo)
for i in range(0,len(list_imgs)):
    path = os.path.join(image_dir,list_imgs[i])
    if os.path.isfile(path):
        image_path = image_dir + list_imgs[i]
        voc_path = list_imgs[i]
        (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
        (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
        annotation_name = nameWithoutExtention + '.xml'
        annotation_path = os.path.join(annotation_dir, annotation_name)
        label_name = nameWithoutExtention + '.txt'
        label_path = os.path.join(yolo_labels_dir, label_name)
    probo = random.randint(1, 100)
    print("Probobility: %d" % probo)
    if(probo < 80): # train dataset
        if os.path.exists(annotation_path):
            train_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, yolov5_images_train_dir + voc_path)
            copyfile(label_path, yolov5_labels_train_dir + label_name)
    else: # test dataset
        if os.path.exists(annotation_path):
            test_file.write(image_path + '\n')
            convert_annotation(nameWithoutExtention) # convert label
            copyfile(image_path, yolov5_images_test_dir + voc_path)
            copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()

运行结束

执行后的项目结构：
放图片的

放标签的：

开始训练

重头开始

单gpu

python train.py --workers 1 --device 0 --batch-size 8 --data data/liver.yaml --img 640 640 --cfg cfg/training/yolov7-MY.yaml --weights 'weights/yolov7.pt' --name yolo
v7 --hyp data/hyp.scratch.p5.yaml

fine-train

权重下载地址：V7各种权重

python train.py --workers 8 --device 0 --batch-size 32 --data data/liver.yaml --img 512 512 --cfg cfg/training/yolov7-my.yaml --weights 'yolov7_training.pt' --name yolov7-fine-train --hyp data/hyp.scratch.p5.yaml

BUG

常见报错1

subprocess.CalledProcessError: Command ‘git tag’ returned non-zero exit status 1.

出现这种情况大概率是传参有问题，参数缺失或者参数错误

这个问题最终是因为找不到yolov7.pt权重文件所以去下载了，但是其实我是有的。可能是因为编码问题。我把我下载 的yolov7.pt重命名（自己手动输一遍）就解决了

常见报错2

这是数据集找不到，执行scirpts/.sh脚本去下载数据集了。。。
liver.yaml中：修改

又报错

查看txt文件

改成绝对路径

在吧后面lable信息删除

就最终报错到，显示没有标签。。。。。。（用我上面的代码这个问题就解决了）

成功训练网络

评价指标

python test.py --data data/voc.yaml --img 512 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights runs/train/exp4/weights/best.pt --name yolov7_640_val

可视化

python detect.py --weights runs/train/exp4/weights/best.pt --conf 0.25 --img-size 512 --source inference/images/a.jpg