kili-technology / Kili Playground
Licence: apache-2.0
Simplest and fastest image and text annotation tool.
Stars: ✭ 166
Projects that are alternatives of or similar to Kili Playground
Cvat
Powerful and efficient Computer Vision Annotation Tool (CVAT)
Stars: ✭ 6,557 (+3850%)
Mutual labels: semantic-segmentation, annotation, labeling
Steal
STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)
Stars: ✭ 424 (+155.42%)
Mutual labels: jupyter-notebook, semantic-segmentation, annotation
Awesome Data Labeling
A curated list of awesome data labeling tools
Stars: ✭ 1,120 (+574.7%)
Mutual labels: semantic-segmentation, annotation, labeling
Label Studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Stars: ✭ 7,264 (+4275.9%)
Mutual labels: semantic-segmentation, annotation, labeling
Multiclass Semantic Segmentation Camvid
Tensorflow 2 implementation of complete pipeline for multiclass image semantic segmentation using UNet, SegNet and FCN32 architectures on Cambridge-driving Labeled Video Database (CamVid) dataset.
Stars: ✭ 67 (-59.64%)
Mutual labels: jupyter-notebook, semantic-segmentation
Slate
A Super-Lightweight Annotation Tool for Experts: Label text in a terminal with just Python
Stars: ✭ 61 (-63.25%)
Mutual labels: annotation, labeling
Unet Tgs
Applying UNET Model on TGS Salt Identification Challenge hosted on Kaggle
Stars: ✭ 81 (-51.2%)
Mutual labels: jupyter-notebook, semantic-segmentation
Deep Residual Unet
ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models.
Stars: ✭ 97 (-41.57%)
Mutual labels: jupyter-notebook, semantic-segmentation
Person Search Annotation
Cross-Platform Annotation Tool for Person Search Datasets
Stars: ✭ 9 (-94.58%)
Mutual labels: annotation, labeling
Spacenet building detection
Project to train/test convolutional neural networks to extract buildings from SpaceNet satellite imageries.
Stars: ✭ 83 (-50%)
Mutual labels: jupyter-notebook, semantic-segmentation
Universal Data Tool
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
Stars: ✭ 1,356 (+716.87%)
Mutual labels: semantic-segmentation, labeling
Diffgram
Data Annotation, Data Labeling, Annotation Tooling, Training Data for Machine Learning
Stars: ✭ 43 (-74.1%)
Mutual labels: annotation, labeling
Deeplabv3 Plus
Tensorflow 2.3.0 implementation of DeepLabV3-Plus
Stars: ✭ 32 (-80.72%)
Mutual labels: jupyter-notebook, semantic-segmentation
Kaggle Carvana Image Masking Challenge
Top 15% ranked solution to the Carvana Image Masking Challenge on Kaggle
Stars: ✭ 13 (-92.17%)
Mutual labels: jupyter-notebook, semantic-segmentation
Autoannotationtool
A label tool aim to reduce semantic segmentation label time, rectangle and polygon annotation is supported
Stars: ✭ 113 (-31.93%)
Mutual labels: semantic-segmentation, annotation
Nova
NOVA is a tool for annotating and analyzing behaviours in social interactions. It supports Annotators using Machine Learning already during the coding process. Further it features both, discrete labels and continuous scores and a visuzalization of streams recorded with the SSI Framework.
Stars: ✭ 110 (-33.73%)
Mutual labels: annotation, labeling
Labelbox
Labelbox is the fastest way to annotate data to build and ship computer vision applications.
Stars: ✭ 1,588 (+856.63%)
Mutual labels: annotation, labeling
Fcn For Semantic Segmentation
Implemention of FCN-8 and FCN-16 with Keras and uses CRF as post processing
Stars: ✭ 155 (-6.63%)
Mutual labels: jupyter-notebook, semantic-segmentation
Pytorch 3dunet
3D U-Net model for volumetric semantic segmentation written in pytorch
Stars: ✭ 765 (+360.84%)
Mutual labels: jupyter-notebook, semantic-segmentation
Image and Text Annotation Tool - Kili Playground
What is Kili Technology?
Kili Technology is an image, text and voice data annotation tool designed to help companies deploy machine learning applications faster. In a few minutes you can start annotating your data thanks to a catalogue of intuitive and configurable interfaces. You can easily accelerate the labeling process by connecting one of your models to pre annotate the data. The work of the annotators is 2 to 5 times faster. Kili Technology facilitates collaboration between technical teams and the business, but also with outsourced annotation companies. Data governance is managed, and production quality control is facilitated. Kili Technology meets the needs of small teams as well as those of large companies with massive stakes.
Kili Technology allows you to:
- Quickly annotate text, images, video, audio and frames (3D images, DICOM Images and scans) thanks to simple and intuitive interfaces
- Easily ingest data, in drag & drop, from your cloud provider, or while keeping your data On Premise, when necessary.
- Manage participants, roles and responsibilities
- Monitor production quality using leading indicators and workflows for production monitoring and data quality validation
- Easily export the produced data
Text annotation example
| Named Entities Extraction and Relation | Rich format support |
|---|---|
 |
 |
Image annotation example
| Classification | Object detection (bounding-box here) |
|---|---|
 |
 |
Video annotation example
| Video annotation | Video classification |
|---|---|
 |
 |
Other interfaces
| Speech to Text | |
|---|---|
 |
 |
What is Kili Playground ?
Kili Playground is a Python client wrapping the GraphQL API of Kili Technology. It allows data scientists and developers to control Kili Technology from an IDE.
Installation
- Clone the repository and install with pip
pip install kili
Get started
- Export an API KEY In
My Account->API KEY:
- In your favourite IDE :
from kili.authentication import KiliAuth
from kili.playground import Playground
kauth = KiliAuth(api_key='MY API KEY')
playground = Playground(kauth)
# You can now play with the playground
You can follow those tutorials to get started :
You can find all of recipes here. Among them:
- How to import assets (run it here)
- How to export labels (run it here)
- How to import predictions (run it here)
- How to query data through the API (run it here)
- How to use AutoML for faster labeling with Kili (run it here)
- How to use Transfer Learning for faster labeling with Kili (run it here)
If you want more details on what you can do with the API, follow the technical documentation.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].