cvpods, a versatile and efficient codebase for many computer vision tasks: classification, segmentation, detection, self-supervised learning, keypoints and 3D(classification / segmentation / detection / representation learing), etc. The aim of cvpods is to achieve efficient experiments management and smooth tasks-switching.
Build cvpods from source
Make sure GPU is available on your local machine.
# Install cvpods with GPU directly
pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git' --user
# Or, to install it with GPU from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
pip install -e cvpods --user
# Or, to build it without GPU from a local clone:
FORCE_CUDA=1 pip install -e cvpods --user
Get Start
Here we use coco object detection task as an example.
# Preprare data path
ln -s /path/to/your/coco/dataset datasets/coco
# Enter a specific experiment dir
cd playground/retinanet/retinanet.res50.fpn.coco.multiscale.1x
# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
OUTPUT_DIR /path/to/your/save_dir # optional
# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"教程和API链接：
We provide a detailed tutorial, which covers introduction, usage, and extend guides in cvpods_tutorials. For all API usages, please refer to our documentation.
