Explore all Data Labeling open source software, libraries, packages, source code, cloud functions and APIs.

Popular New Releases in Data Labeling

label-studio

Release 1.4.1

cvat

v1.7.0

universal-data-tool

v0.14.26

semantic-segmentation-editor

1.6.0

scalabel

Pre-release for 0.3.0

Popular Libraries in Data Labeling

label-studio

by heartexlabs doticonpythondoticon

star image 8078 doticonApache-2.0

Label Studio is a multi-type data labeling and annotation tool with standardized output format

cvat

by openvinotoolkit doticontypescriptdoticon

star image 6542 doticonNOASSERTION

Powerful and efficient Computer Vision Annotation Tool (CVAT)

VoTT

by microsoft doticontypescriptdoticon

star image 3348 doticonMIT

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

cloud-annotations

by cloud-annotations doticontypescriptdoticon

star image 2616 doticonMIT

🐝 A fast, easy and collaborative open source image annotation tool for teams and individuals.

labelbox

by Labelbox doticonjavascriptdoticon

star image 1562 doticonApache-2.0

Labelbox is the fastest way to annotate data to build and ship computer vision applications.

universal-data-tool

by UniversalDataTool doticonjavascriptdoticon

star image 1429 doticonMIT

Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.

semantic-segmentation-editor

by Hitachi-Automotive-And-Industry-Lab doticonjavascriptdoticon

star image 1155 doticonMIT

Web labeling tool for bitmap images and point clouds

BBox-Label-Tool

by puzzledqs doticonpythondoticon

star image 1059 doticonMIT

A simple tool for labeling object bounding boxes in images

sloth

by cvhciKIT doticonpythondoticon

star image 588 doticonNOASSERTION

Sloth is a tool for labeling image and video data for computer vision research.

Trending New libraries in Data Labeling

bbox-visualizer

by shoumikchow doticonpythondoticon

star image 251 doticonMIT

Make drawing and labeling bounding boxes easy as cake

hover

by phurwicz doticonpythondoticon

star image 184 doticonMIT

:speedboat: Never spend O(n) to annotate data again. Fun and precision come free.

nota

by DeNA doticonjavascriptdoticon

star image 102 doticonMIT

Web application for image and video labeling and annotation

label-studio-ml-backend

by heartexlabs doticonpythondoticon

star image 84 doticonApache-2.0

Configs and boilerplates for Label Studio's Machine Learning backend

FewShotMultiLabel

by AtmaHou doticonpythondoticon

star image 52 doticon

Code for AAAI2021 paper: Few-Shot Learning for Multi-label Intent Detection.

PyQt-image-annotation-tool

by robertbrada doticonpythondoticon

star image 45 doticon

Tool for assigning labels to images from a given folder.

cclabeler

by Elin24 doticonjavascriptdoticon

star image 43 doticon

A web tool for labeling pedestrians in an image, provideing two types of label: box and point.

dc20_labels

by clemenko doticonpythondoticon

star image 24 doticon

dockercon 2020 talk - labels

superannotate-python-sdk

by superannotateai doticonpythondoticon

star image 23 doticonMIT

SuperAnnotate Python SDK

Top Authors in Data Labeling

1

UniversalDataTool

3 Libraries

star icon1435

2

heartexlabs

3 Libraries

star icon8237

3

wbap

2 Libraries

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4

IETF-Hackathon

2 Libraries

star icon9

5

microsoft

2 Libraries

star icon3369

6

Elin24

2 Libraries

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7

CH-YYK

2 Libraries

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8

doccano

2 Libraries

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9

aws-samples

2 Libraries

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10

mainzed

2 Libraries

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1

3 Libraries

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2

3 Libraries

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3

2 Libraries

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4

2 Libraries

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5

2 Libraries

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6

2 Libraries

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7

2 Libraries

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8

2 Libraries

star icon50

9

2 Libraries

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10

2 Libraries

star icon5

Trending Kits in Data Labeling

Recommender systems are becoming more and more popular in eCommerce. Amazon, Netflix, and Zalando have all implemented advanced recommender systems to suggest products to users. A recommender system intends to predict user preferences based on their past behavior and propose items that may be of interest to them. This can be anything from movies to music and books. Recommendation engines are used everywhere, with the main objective of boosting customer engagement and sales. Python is a very popular programming language for machine learning. Scikit-learn, a Python library for machine learning can be used to build recommender systems. One can implement different machine learning algorithms in scikit-learn and build recommender systems. There are various other Python libraries also available that can be used to build recommender systems. In this kit, we have listed the best Python libraries for building recommendation systems.

Data labeling is the task of giving a meaningful label to your data sample. It's usually done by humans to assign tags to text, images, and videos. Once labeled, the data can be used for training supervised machine learning algorithms such as classification and object detection. Here are nine open source tools with Java interfaces to do the job. A data labeler is an interface provided by a machine learning library to label data. A data labeler shows you a data point and allows you to specify a label for that data point. If you are building a classification model, you can use a data labeler to specify the class of each data point. The labeled data points are then used as training examples in a classifier algorithm. In this kit, we will look at 9 of the best Java Data Labeling libraries.

The data labelling industry is maturing quickly. This has led to an explosion of new tools, data labelling libraries and platforms for training machine learning models over the past few years. Python is the most popular programming language for Data Science. It is very easy to learn and there are many applications of it in the field of Data Science. Python has many libraries for Machine Learning and Data Science. Popular open source Python libraries include: Pandas - pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Label-studio - Label Studio is a multitype data labeling and annotation tool. In this kit, you can find 10 best Python Data Labelling libraries, that can be used to train your machine learning algorithms.

With Annotation libraries, you can easily annotate (categorize, label, tag) large number of images or videos using machine learning. This is useful if you need to teach your computer how to automatically recognize certain objects in your images. The resulting model can be used for a variety of purposes like image filtering, object detection and recognition. All the annotations are stored in convenient JSON files so you can easily customize the front end. The need for JavaScript Data Labelling has greatly increased these days due to the rapid growth of machine learning and deep learning technologies. We have used a lot of JavaScript Data Labelling libraries these days, but some of them are more popular than others. The following is a comprehensive list of the best open source libraries.

The C++ language is a popular choice for computer programming. It’s an object-oriented language, but still has low-level memory access like C does. One of the things that makes it so popular is the sheer number of libraries that are available to add functionality to C++ programs. One category of libraries you should look at is data labelling tools. C++ Data Labelling libraries are a great way to accelerate the annotation process for your machine learning project. There are several popular open-source libraries available for developers: ProGraML - Graphbased Program Representation & Models for Deep Learning; video-content-description-VCD - a metadata format designed to enable the description of scene information, particularly efficient for discrete data series, such as image or point-cloud sequences from sensor data; Camera-capture - GUI tool for collecting & labeling data from live camera feed. Full list of the best open source libraries below.

Data labelling can be used to add tags to images, labels to audio files or even annotations for video content. It's particularly useful for computer vision applications, such as facial recognition or object detection. It's also a necessary step when training AI models which will later be used in critical applications, like medical imaging systems and self-driving cars. There is a wealth of data labelling tools out there, some of which offer more features than others, while others are built with a specific need in mind. Developers tend to use some of the following open source libraries: BMW-Labeltool-Lite - This repository provides you with an easy to use labeling tool for State-of-the-art Deep Learning training purposes, SynthDet - An endtoend object detection pipeline using synthetic data, Alturos.ImageAnnotation - A collaborative tool for labeling image data for yolo. Find the following best 9 C# Data Labelling libraries:

Go is a general purpose language developed by google. Go can be used to build server side applications, APIs and web services. Go is also used in machine learning and data science projects. In this article, I will list few of the best Golang data labelling libraries. Go vector space models package is built on top of gonum. This kit provides an implementation of some of the commonly used algorithms in natural language processing (NLP) like word2vec, doc2vec etc. With these libraries, you can convert your texts into vectors which can then be used as features in classification and regression models to solve text classification problems. A few of the most popular open source libraries for developers are: Parca - Continuous profiling for analysis of CPU, memory usage over time, and down to the line number. Saving infrastructure cost, improving performance, and increasing reliability; Etable - provides a DataTable / DataFrame structure in Go (golang), similar to pandas and xarray in Python, and Apache Arrow Table, using etensor n-dimensional columns aligned by common outermost row dimension. The following is a comprehensive list of the best open source libraries for Go data labelling:

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

1import pandas as pd
2from enum import Enum
3
4
5df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
6df
7
8class Tipos(Enum):
9    B = 1
10    I = 2
11    L = 3
12
13for index, row in df.iterrows():
14    sentencas = row.values
15    for sentenca in sentencas:
16        for pos, palavra in enumerate(sentenca.split()):
17            print(f"{palavra} {Tipos(pos+1).name}")
18
19

Results:

1import pandas as pd
2from enum import Enum
3
4
5df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
6df
7
8class Tipos(Enum):
9    B = 1
10    I = 2
11    L = 3
12
13for index, row in df.iterrows():
14    sentencas = row.values
15    for sentenca in sentencas:
16        for pos, palavra in enumerate(sentenca.split()):
17            print(f"{palavra} {Tipos(pos+1).name}")
18
19                first              second
200   product and other  product and prices
211  product2 and other              price2
222               price  product3 and price
23
24product B
25and I
26other L
27product B
28and I
29prices L
30product2 B
31and I
32other L
33price2 B
34price B
35product3 B
36and I
37price L
38

Desired Results:

1import pandas as pd
2from enum import Enum
3
4
5df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
6df
7
8class Tipos(Enum):
9    B = 1
10    I = 2
11    L = 3
12
13for index, row in df.iterrows():
14    sentencas = row.values
15    for sentenca in sentencas:
16        for pos, palavra in enumerate(sentenca.split()):
17            print(f"{palavra} {Tipos(pos+1).name}")
18
19                first              second
200   product and other  product and prices
211  product2 and other              price2
222               price  product3 and price
23
24product B
25and I
26other L
27product B
28and I
29prices L
30product2 B
31and I
32other L
33price2 B
34price B
35product3 B
36and I
37price L
38        Word Ent
390    product B_first
401        and I_first
412      other L_first
423    product B_second
434        and I_second
445     prices L_second
456   product2 B_first
467        and I_first
478      other L_first
489     price2 B_second
4910     price B_first
5011  product3 B_second
5112       and I_second
5213     price L_second
53
54# In that case, the sequence is like that: (B_first, I_first, L_first, L_first...) and if changes the column gets B_second, I_second, L_second...
55

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

1import pandas as pd
2from enum import Enum
3
4
5df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
6df
7
8class Tipos(Enum):
9    B = 1
10    I = 2
11    L = 3
12
13for index, row in df.iterrows():
14    sentencas = row.values
15    for sentenca in sentencas:
16        for pos, palavra in enumerate(sentenca.split()):
17            print(f"{palavra} {Tipos(pos+1).name}")
18
19                first              second
200   product and other  product and prices
211  product2 and other              price2
222               price  product3 and price
23
24product B
25and I
26other L
27product B
28and I
29prices L
30product2 B
31and I
32other L
33price2 B
34price B
35product3 B
36and I
37price L
38        Word Ent
390    product B_first
401        and I_first
412      other L_first
423    product B_second
434        and I_second
445     prices L_second
456   product2 B_first
467        and I_first
478      other L_first
489     price2 B_second
4910     price B_first
5011  product3 B_second
5112       and I_second
5213     price L_second
53
54# In that case, the sequence is like that: (B_first, I_first, L_first, L_first...) and if changes the column gets B_second, I_second, L_second...
55out = df.unstack().str.split().explode().sort_index(level=1).to_frame('Word')
56out['Ent'] = out.groupby(level=[0, 1]).cumcount().map(Tipos) \
57                 + '_' + out.index.get_level_values(0)
58out = out.reset_index(drop=True)
59

Output:

1import pandas as pd
2from enum import Enum
3
4
5df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
6df
7
8class Tipos(Enum):
9    B = 1
10    I = 2
11    L = 3
12
13for index, row in df.iterrows():
14    sentencas = row.values
15    for sentenca in sentencas:
16        for pos, palavra in enumerate(sentenca.split()):
17            print(f"{palavra} {Tipos(pos+1).name}")
18
19                first              second
200   product and other  product and prices
211  product2 and other              price2
222               price  product3 and price
23
24product B
25and I
26other L
27product B
28and I
29prices L
30product2 B
31and I
32other L
33price2 B
34price B
35product3 B
36and I
37price L
38        Word Ent
390    product B_first
401        and I_first
412      other L_first
423    product B_second
434        and I_second
445     prices L_second
456   product2 B_first
467        and I_first
478      other L_first
489     price2 B_second
4910     price B_first
5011  product3 B_second
5112       and I_second
5213     price L_second
53
54# In that case, the sequence is like that: (B_first, I_first, L_first, L_first...) and if changes the column gets B_second, I_second, L_second...
55out = df.unstack().str.split().explode().sort_index(level=1).to_frame('Word')
56out['Ent'] = out.groupby(level=[0, 1]).cumcount().map(Tipos) \
57                 + '_' + out.index.get_level_values(0)
58out = out.reset_index(drop=True)
59>>> out
60        Word       Ent
610    product   B_first
621        and   I_first
632      other   L_first
643    product  B_second
654        and  I_second
665     prices  L_second
676   product2   B_first
687        and   I_first
698      other   L_first
709     price2  B_second
7110     price   B_first
7211  product3  B_second
7312       and  I_second
7413     price  L_second
75

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

1        datacolumn1
2 row 1  knowledge; information; data
3 row 2  digital;transmission; interoperability; data labeling
4 row 3  library catalogs; libraries; mobile;libraries
5

I need this output:

1        datacolumn1
2 row 1  knowledge; information; data
3 row 2  digital;transmission; interoperability; data labeling
4 row 3  library catalogs; libraries; mobile;libraries
5       datacolumn1
6row 1  knowledge; information; data
7row 2  digital transmission; interoperability; data labeling
8row 3  library catalogs; libraries; mobile libraries
9

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

1        datacolumn1
2 row 1  knowledge; information; data
3 row 2  digital;transmission; interoperability; data labeling
4 row 3  library catalogs; libraries; mobile;libraries
5       datacolumn1
6row 1  knowledge; information; data
7row 2  digital transmission; interoperability; data labeling
8row 3  library catalogs; libraries; mobile libraries
9library(stringr)
10str_replace_all(datacolumn1, "(\\w);(\\w)", "\\1 \\2")
11

This is probably a neater regex out there, but this will do!

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.


Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

1import azureml.core
2from azureml.core import Dataset, Datastore, Workspace
3
4# Load in work space from saved config file
5ws = Workspace.from_config()
6
7# Define full dataset of interest and retrieve it
8dataset_name = 'complete_2017'
9data = Dataset.get_by_name(ws, dataset_name)
10
11# Extract file references from dataset as relative paths
12rel_filepaths = data.to_path()
13
14# Stitch back in base directory path to get a list of absolute paths
15src_folder = '/raw-data/2017'
16abs_filepaths = [src_folder + path for path in rel_filepaths]
17
18# Define regular expression pattern for extracting source section
19import re
20pattern = re.compile('\/(S.+)_image\d+.jpg')
21
22# Create new list of all unique source sections
23sections = sorted(set([m.group(1) for m in map(pattern.match, rel_filepaths) if m]))
24
25# Randomly select sections
26num_sections = 5
27set_seed = 221020
28random.seed(set_seed)   # for repeatibility
29sample_sections = random.choices(sections, k = num_sections)
30
31# Extract images related to the selected sections
32matching_images = [filename for filename in abs_filepaths if any(section in filename for section in sample_sections)]
33
34# Define datastore of interest
35datastore = Datastore.get(ws, 'ml-datastore')
36
37# Convert string paths to Azure Datapath objects and relate back to datastore
38from azureml.data.datapath import DataPath
39datastore_path = [DataPath(datastore, filepath) for filepath in matching_images]
40
41# Generate new dataset using from_files() and filtered list of paths
42sample = Dataset.File.from_files(datastore_path)
43
44sample_name = 'random-section-sample'
45sample_dataset = sample.register(workspace = ws, name = sample_name, description = 'Sampled sections from full dataset using set seed.')
46

Issue: The code I've written in Python SDK runs and the new FileDataset registers, but when I try to look at the dataset details or use it for a Data Labeling project I get the following error even as Owner.

1import azureml.core
2from azureml.core import Dataset, Datastore, Workspace
3
4# Load in work space from saved config file
5ws = Workspace.from_config()
6
7# Define full dataset of interest and retrieve it
8dataset_name = 'complete_2017'
9data = Dataset.get_by_name(ws, dataset_name)
10
11# Extract file references from dataset as relative paths
12rel_filepaths = data.to_path()
13
14# Stitch back in base directory path to get a list of absolute paths
15src_folder = '/raw-data/2017'
16abs_filepaths = [src_folder + path for path in rel_filepaths]
17
18# Define regular expression pattern for extracting source section
19import re
20pattern = re.compile('\/(S.+)_image\d+.jpg')
21
22# Create new list of all unique source sections
23sections = sorted(set([m.group(1) for m in map(pattern.match, rel_filepaths) if m]))
24
25# Randomly select sections
26num_sections = 5
27set_seed = 221020
28random.seed(set_seed)   # for repeatibility
29sample_sections = random.choices(sections, k = num_sections)
30
31# Extract images related to the selected sections
32matching_images = [filename for filename in abs_filepaths if any(section in filename for section in sample_sections)]
33
34# Define datastore of interest
35datastore = Datastore.get(ws, 'ml-datastore')
36
37# Convert string paths to Azure Datapath objects and relate back to datastore
38from azureml.data.datapath import DataPath
39datastore_path = [DataPath(datastore, filepath) for filepath in matching_images]
40
41# Generate new dataset using from_files() and filtered list of paths
42sample = Dataset.File.from_files(datastore_path)
43
44sample_name = 'random-section-sample'
45sample_dataset = sample.register(workspace = ws, name = sample_name, description = 'Sampled sections from full dataset using set seed.')
46Access denied: Failed to authenticate data access with Workspace system assigned identity. Make sure to add the identity as Reader of the data service.
47

Additionally, under the details tab Files in dataset is Unknown and Total size of files in dataset is Unavailable.

I haven't come across this issue anywhere else. I'm able to generate datasets in other ways, so I suspect it's an issue with the code given that I'm working with the data in an unconventional way.


Additional Notes:

  • Azure ML version is 1.15.0

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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