deepdataspace 0.12.2

A tool for CV dataset labeling, visualizing and analysing

DeepDataSpace

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The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis.

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Website • Docs • Tutorials •

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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 OS: ✅ x86/x64, ✅ arm64
• Windows 10: ✅ x86/x64, ❌ arm64
• Ubuntu LTS since 18.04: ✅ x86/x64, ❌ arm64
• Docker Compose: ✅ x86/x64, ✅ arm64

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:

https://user-images.githubusercontent.com/10917115/240788538-f1fa8d52-7d93-4fe3-bf42-55284074febd.mp4

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 by Docker Compose

# clone the source code
git clone https://github.com/IDEA-Research/deepdataspace.git

# prepare dataset directory(where you put all your datasets inside)
mkdir -p datasets
export DDS_DATASET_DIR=$PWD/datasets

# choose a visiting port for DDS
export DDS_PORT=8765

# start DDS with docker compose
cd deepdataspace
docker compose up

If everything goes well, you can start visiting DDS at http://127.0.0.1:8765

4. Documentation

Visit our documentation for more details on how to utilize the powers of DDS.

• Quick Start
• Tutorials
• API Reference

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.