A tool for CV dataset labeling, visualizing and analysing
Project description
DeepDataSpace
The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis.
Deep Data Space (DDS) is an open-source dataset tool with these features out-of-box:
- interactive dataset visualization and exploration
- intelligent annotation with a collaborative workflow
- efficient model management and performance analysis
1. Installation
1.1 Prerequisites
DeepDataSpace(DDS) requires Python 3.8 - 3.10 and runs on the following platforms:
- Mac, x86/arm64
- Windows 10, x86/x64
- Ubuntu 18.04/20.04/22.04, x86/x64
1.2 Installing from PyPI
python3 -m pip install pip --upgrade
python3 -m pip install deepdataspace
2. Quick Start
The dds
command will be available once the deepdataspace
is installed, with which you can quickly start the DDS
tool.
dds --quickstart
# Started DDS[${pid}] at http://127.0.0.1:8765.
# The DDS tool is importing datasets inside dir in the background: $HOME/.deepdataspace/dataset-samples.
# Explore other useful commands by: ddsop --help.
# You can quit the DDS tool with Ctrl+C.
It takes a while the first time you start the DDS tool, as it is downloading extra dependencies to set up a runtime
environment.
Once the DDS tool is started, visit http://127.0.0.1:8765 and you will see the flowing sample datasets:
3. Alternative Installation Methods
3.1 Installing from Source Code
# clone the source code
git clone https://github.com/IDEA-Research/deepdataspace.git
# prepare the node environment(if you haven't installed the Pnpm and Node environment yet)
curl -fsSL https://get.pnpm.io/install.sh | sh -
pnpm env use --global lts
# compile frontend files
pnpm i
pnpm run build:app
# copy frontend files to python package dir
rm -rf deepdataspace/server/static/*
cp -R packages/app/dist/* deepdataspace/server/static/
cp deepdataspace/server/static/index.html deepdataspace/server/templates/
# install the package
python3 -m pip install pip --upgrade
python3 -m pip install -r requirements.txt
python3 setup.py install
After the installation, you can start DDS the same way as above:
dds --quickstart
3.2 Installing from Docker Image
Step 1: Preparation
# pull the latest docker image
docker pull deepdataspace/dds
# create a docker volume for dds to persistent data
docker volume create dds-runtime
# choose a visiting port for DDS
export DDS_PORT=8765
Step 2: Start DDS in quickstart mode
# start the DDS in quickstart mode
# DDS will download some sample datasets and import them
docker run -it --name dds --rm \
-p $DDS_PORT:8765 \
-v dds-runtime:/dds/runtime \
deepdataspace/dds \
dds --quickstart -V
If everything goes well, you can start visit DDS at http://127.0.0.1:8765
Step 3: Mount your dataset directory(Optional)
If you start DDS in quickstart
mode, DDS will try to download some sample datasets and import them for you.
But most frequently, you want DDS to import your local dataset files. This is possible by mouting your local dataset directory to /dds/datasets
inside container.
# assume $PWD/datasets is your local dataset directory
mkdir -p datasets
# start the container without quickstart mode
docker run -it --name dds --rm \
-p 54321:8765 \
-v dds-runtime:/dds/runtime \
-v $PWD/datasets:/dds/datasets:ro \
deepdataspace/dds \
dds -V
4. Documentation
Visit our documentation for more details on how to utilize the powers of DDS.
5. Uninstallation
For users who installed DDS from PyPi or source code, just uninstall DDS with pip
and delete the runtime files.
pip uninstall deepdataspace
rm -rf ~/.deepdataspace/* # use with caution, it will delete all datasets imported before
For users who installed DDS from docker image, just stop the container and remove the docker image and volume.
docker stop dds
docker rmi deepdataspace/dds
docker volume remove dds-runtime # use with caution, it will delete all datasets imported before
6. License
This project is released under the Apache 2.0 License.
Copyright 2023-present, IDEA
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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