yolov7环境安装

Ubuntu 使用conda安装python虚拟环境并进行yolo-fast训练_Y_Hungry的博客-CSDN博客安装condaUbuntu 使用conda安装python虚拟环境并进行yolo-fast训练_Y_Hungry的博客-CSDN博客

conda create -n yolov7_env python=3.8

git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
conda activate yolov7_env

修改一下requirements.txt:

# 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,!=1.12.0
#torchvision>=0.8.1,!=0.13.0
tqdm>=4.41.0
protobuf<4.21.3

# Logging -------------------------------------
tensorboard>=2.4.1
# wandb

# Plotting ------------------------------------
pandas>=1.1.4
seaborn>=0.11.0

# Export --------------------------------------
# coremltools>=4.1  # CoreML export
# onnx>=1.9.0  # ONNX export
# onnx-simplifier>=0.3.6  # ONNX simplifier
# scikit-learn==0.19.2  # CoreML quantization
# tensorflow>=2.4.1  # TFLite export
# tensorflowjs>=3.9.0  # TF.js export
# openvino-dev  # OpenVINO export

# Extras --------------------------------------
ipython  # interactive notebook
psutil  # system utilization
thop  # FLOPs computation
# albumentations>=1.0.3
# pycocotools>=2.0  # COCO mAP
# roboflow

注释掉

#trorch

#torchvision

yolov7环境安装_第1张图片

pip install -r requirements.txt

单独安装torch、torchvision

下载地址两者对应关系一般是torch1.7.0-torchvision0.8.0、torch1.8.0-torchvision0.9.0、torch1.9.0-torchvision0.10.0
例如(cu111表示cuda-11.1,cp38表示python3.8):

pip install torch-1.8.0+cu111-cp38-cp38-linux_x86_64.whl

pip install torchvision-0.9.0+cu111-cp38-cp38-linux_x86_64.whl

yolov7环境安装_第2张图片

yolov7环境安装_第3张图片

 至此环境搭建好了

测试一下

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

yolov7环境安装_第4张图片

yolov7环境安装_第5张图片

 

你可能感兴趣的:(深度学习,python,开发语言)