title: yolov5和yolov5-face环境搭建和常见踩坑
banner_img: https://cloud.studyinglover.com/api/raw/?path=/photos/blog/33a1d238f41bcd6994390b5a52067cd6.png
date: 2023-2-7 16:49:00
categories:
- 踩坑
tags:
- 机器视觉
yolov5环境搭建
在随便哪新建一个requirements.txt文件
内容是
# YOLOv5 requirements
# Usage: pip install -r requirements.txt
# Base ----------------------------------------
matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.1.1
Pillow>=7.1.2
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.64.0
protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
# Logging -------------------------------------
tensorboard>=2.4.1
# wandb
# clearml
# Plotting ------------------------------------
pandas>=1.1.4
seaborn>=0.11.0
# Export --------------------------------------
# coremltools>=5.2 # CoreML export
# onnx>=1.9.0 # ONNX export
# onnx-simplifier>=0.4.1 # ONNX simplifier
# nvidia-pyindex # TensorRT export
# nvidia-tensorrt # TensorRT export
# scikit-learn==0.19.2 # CoreML quantization
# tensorflow>=2.4.1 # TFLite export (or tensorflow-cpu, tensorflow-aarch64)
# tensorflowjs>=3.9.0 # TF.js export
# openvino-dev # OpenVINO export
# Extras --------------------------------------
ipython # interactive notebook
psutil # system utilization
thop>=0.1.1 # FLOPs computation
# albumentations>=1.0.3
# pycocotools>=2.0 # COCO mAP
# roboflow
然后在当前目录下打开命令行，创建一个环境
conda create -n yolov5 python
创建好环境之后，激活环境
conda activate yolov5
然后安装依赖
pip install -r requirements.txt
安装完成后代码就可以运行了
划分数据集
新建一个split_train_val.py文件，内容如下
import os
import shutil
import random
def split_dataset(src_folder, dest_folder, ratio):
images_folder = os.path.join(src_folder, "images")
labels_folder = os.path.join(src_folder, "labels")
if not os.path.exists(images_folder) or not os.path.exists(labels_folder):
raise Exception("Source folder doesn't exist.")
if not os.path.exists(dest_folder):
os.makedirs(dest_folder)
train_folder = os.path.join(dest_folder, "train")
val_folder = os.path.join(dest_folder, "val")
if not os.path.exists(train_folder):
os.makedirs(train_folder)
if not os.path.exists(val_folder):
os.makedirs(val_folder)
train_images_folder = os.path.join(train_folder, "images")
train_labels_folder = os.path.join(train_folder, "labels")
val_images_folder = os.path.join(val_folder, "images")
val_labels_folder = os.path.join(val_folder, "labels")
if not os.path.exists(train_images_folder):
os.makedirs(train_images_folder)
if not os.path.exists(train_labels_folder):
os.makedirs(train_labels_folder)
if not os.path.exists(val_images_folder):
os.makedirs(val_images_folder)
if not os.path.exists(val_labels_folder):
os.makedirs(val_labels_folder)
images = [f for f in os.listdir(images_folder) if f.endswith(".bmp") ]
num_images = len(images)
for i, image in enumerate(images):
image_path = os.path.join(images_folder, image)
label_path = os.path.join(labels_folder, os.path.splitext(image)[0] + ".txt")
if random.uniform(0, 1) < ratio:
dest_images_folder = train_images_folder
dest_labels_folder = train_labels_folder
else:
dest_images_folder = val_images_folder
dest_labels_folder = val_labels_folder
shutil.copy2(image_path, os.path.join(dest_images_folder, image))
shutil.copy2(label_path, os.path.join(dest_labels_folder, os.path.splitext(image)[0] + ".txt"))
print("Copied {}/{} images".format(i + 1, num_images))
if __name__ == "__main__":
src_folder = ""# 原始数据集的路径
dest_folder = ""# 分割后的数据集的路径
ratio = 0.8 # 将 80% 的图片分到训练集，20% 的图片分到验证集
split_dataset(src_folder, dest_folder, ratio)
划分训练集和验证集，运行split_train_val.py，传入刚才保存的文件夹路径，会将图片和标签划分到一个新的文件夹
- data
- train
- images
- labels
- val
- images
- labels
yolov5常见踩坑
not enough values to unpack (expected 2, got 0)
如图
我们需要检查一下我们标记的txt文件
举个例子
这是我们需要的标记格式
0 0.5 0.5 0.5 0.5
这是错误的标注格式
0 0.5 0.5 0.5 0.5
问题就出在了最后一行的\n上，我们删除最后一行就可以了。我用chatGPT写了一个函数来做这件事
# 去除txt文件中的空行
def remove_empty_lines(file_path):
with open(file_path, 'r') as f:
lines = f.readlines()
with open(file_path, 'w') as f:
for line in lines:
if len(line)>3:
f.write(line)
AssertionError: No results.txt files found in /content/yolov5-face/runs/train/exp, nothing to plot.
Traceback (most recent call last):
File "train.py", line 513, in
train(hyp, opt, device, tb_writer, wandb)
File "train.py", line 400, in train
plot_results(save_dir=save_dir) # save as results.png
File "/content/yolov5-face/utils/plots.py", line 393, in plot_results
assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
AssertionError: No results.txt files found in /content/yolov5-face/runs/train/exp, nothing to plot.
出现这个问题的原因是此代码块未运行
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
如果你只使用单 GPU 并设置 epoch <20，这个块将不起作用。解决方案是设置epoch>20。
gitpython找不到对应版本
ERROR: Could not find a version that satisfies the requirement gitpython>=3.1.30 (from versions: 0.1.7, 0.2.0b1, 0.3.0b1, 0.3.0b2, 0.3.1b2, 0.3.2rc1, 0.3.2, 0.3.2.1, 0.3.3, 0.3.4, 0.3.5, 0.3.6, 0.3.7, 1.0.0, 1.0.1, 1.0.2, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.0.5, 2.0.6, 2.0.7, 2.0.8, 2.0.9.dev0, 2.0.9.dev1, 2.0.9, 2.1.0, 2.1.1, 2.1.3, 2.1.4, 2.1.5, 2.1.6, 2.1.7, 2.1.8, 2.1.9, 2.1.10, 2.1.11, 2.1.12, 2.1.13, 2.1.14, 2.1.15, 3.0.0, 3.0.1, 3.0.2, 3.0.3, 3.0.4, 3.0.5, 3.0.6, 3.0.7, 3.0.8, 3.0.9, 3.1.0, 3.1.1, 3.1.2, 3.1.3, 3.1.4, 3.1.5, 3.1.6, 3.1.7, 3.1.8, 3.1.9, 3.1.10, 3.1.11, 3.1.12, 3.1.13, 3.1.14, 3.1.15, 3.1.16, 3.1.17, 3.1.18, 3.1.19, 3.1.20)
ERROR: No matching distribution found for gitpython>=3.1.30
python版本过低，升级即可。