https://github.com/broadinstitute/keras-rcnn
Keras package for region-based convolutional neural networks (RCNNs)
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
4 of 21 committers (19.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (6.7%) to scientific vocabulary
Keywords
cntk
deep-learning
image-segmentation
object-detection
tensorflow
theano
Keywords from Contributors
distributed
deep-neural-networks
Last synced: 5 months ago
·
JSON representation
Repository
Keras package for region-based convolutional neural networks (RCNNs)
Basic Info
Statistics
- Stars: 556
- Watchers: 38
- Forks: 220
- Open Issues: 71
- Releases: 0
Topics
cntk
deep-learning
image-segmentation
object-detection
tensorflow
theano
Created almost 9 years ago
· Last pushed almost 6 years ago
Metadata Files
Readme
License
README.rst
Keras-RCNN
==========
.. image:: https://travis-ci.org/broadinstitute/keras-rcnn.svg?branch=master
:target: https://travis-ci.org/broadinstitute/keras-rcnn
.. image:: https://codecov.io/gh/broadinstitute/keras-rcnn/branch/master/graph/badge.svg
:target: https://codecov.io/gh/broadinstitute/keras-rcnn
keras-rcnn is *the* Keras package for region-based convolutional
neural networks.
Requirements
---------------
Python 3
keras-resnet==0.2.0
numpy==1.16.2
tensorflow==1.13.1
Keras==2.2.4
scikit-image==0.15.0
Getting Started
---------------
Let’s read and inspect some data:
.. code:: python
training_dictionary, test_dictionary = keras_rcnn.datasets.shape.load_data()
categories = {"circle": 1, "rectangle": 2, "triangle": 3}
generator = keras_rcnn.preprocessing.ObjectDetectionGenerator()
generator = generator.flow_from_dictionary(
dictionary=training_dictionary,
categories=categories,
target_size=(224, 224)
)
validation_data = keras_rcnn.preprocessing.ObjectDetectionGenerator()
validation_data = validation_data.flow_from_dictionary(
dictionary=test_dictionary,
categories=categories,
target_size=(224, 224)
)
target, _ = generator.next()
target_bounding_boxes, target_categories, target_images, target_masks, target_metadata = target
target_bounding_boxes = numpy.squeeze(target_bounding_boxes)
target_images = numpy.squeeze(target_images)
target_categories = numpy.argmax(target_categories, -1)
target_categories = numpy.squeeze(target_categories)
keras_rcnn.utils.show_bounding_boxes(target_images, target_bounding_boxes, target_categories)
Let’s create an RCNN instance:
.. code:: python
model = keras_rcnn.models.RCNN((224, 224, 3), ["circle", "rectangle", "triangle"])
and pass our preferred optimizer to the `compile` method:
.. code:: python
optimizer = keras.optimizers.Adam(0.0001)
model.compile(optimizer)
Finally, let’s use the `fit_generator` method to train our network:
.. code:: python
model.fit_generator(
epochs=10,
generator=generator,
validation_data=validation_data
)
External Data
-------------
The data is made up of a list of dictionaries corresponding to images.
* For each image, add a dictionary with keys 'image', 'objects'
* 'image' is a dictionary, which contains keys 'checksum', 'pathname', and 'shape'
* 'checksum' is the md5 checksum of the image
* 'pathname' is the pathname of the image, put in full pathname
* 'shape' is a dictionary with keys 'r', 'c', and 'channels'
* 'c': number of columns
* 'r': number of rows
* 'channels': number of channels
* 'objects' is a list of dictionaries, where each dictionary has keys 'bounding_box', 'category'
* 'bounding_box' is a dictionary with keys 'minimum' and 'maximum'
* 'minimum': dictionary with keys 'r' and 'c'
* 'r': smallest bounding box row
* 'c': smallest bounding box column
* 'maximum': dictionary with keys 'r' and 'c'
* 'r': largest bounding box row
* 'c': largest bounding box column
* 'category' is a string denoting the class name
Suppose this data is save in a file called training.json. To load data,
.. code:: python
import json
with open('training.json') as f:
d = json.load(f)
Slack
-----
We’ve been meeting in the #keras-rcnn channel on the keras.io Slack
server.
You can join the server by inviting yourself from the following website:
https://keras-slack-autojoin.herokuapp.com/
Owner
- Name: Broad Institute
- Login: broadinstitute
- Kind: organization
- Location: Cambridge, MA
- Website: http://www.broadinstitute.org/
- Twitter: broadinstitute
- Repositories: 1,083
- Profile: https://github.com/broadinstitute
Broad Institute of MIT and Harvard
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Allen Goodman | a****n@i****m | 343 |
| jhung0 | j****g@b****g | 175 |
| Matthieu Broisin | m****n@w****g | 66 |
| Matthieu Broisin | m****n@b****g | 58 |
| JihongJu | j****n@g****m | 19 |
| lee yeong khang | y****e@v****m | 12 |
| jihongju | d****u@g****m | 7 |
| Mihai Morariu | m****u@d****m | 6 |
| JihongJu | J****u | 4 |
| Morteza Milani | m****i@g****m | 2 |
| Yann Henon | y****n@g****m | 2 |
| Hans Gaiser | j****r@d****m | 2 |
| Branden Murray | b****y@g****m | 1 |
| Christopher Akroyd | c****s@c****m | 1 |
| Devin R Waltman | d****n@g****m | 1 |
| Guilherme Campos | g****s@g****m | 1 |
| Allen Goodman | a****n@w****g | 1 |
| Hanna Rudakouskaya | h****a@g****m | 1 |
| Yanfeng Liu | y****x@g****m | 1 |
| akshaybapat04 | a****4@g****m | 1 |
| imparkss | i****s@n****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 64
- Total pull requests: 36
- Average time to close issues: 27 days
- Average time to close pull requests: 5 months
- Total issue authors: 39
- Total pull request authors: 12
- Average comments per issue: 2.22
- Average comments per pull request: 1.28
- Merged pull requests: 29
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- 0x00b1 (10)
- jhung0 (9)
- milani (2)
- zzdang (2)
- mmiisskk (2)
- Genie-Liu (2)
- brandenkmurray (2)
- willowxh (2)
- JihongJu (2)
- dberma15 (2)
- aseylys (1)
- worldmovers (1)
- stylishsam (1)
- yhenon (1)
- shobhitpuri (1)
Pull Request Authors
- jhung0 (14)
- 0x00b1 (10)
- drwaltman (2)
- mbroisinBI (2)
- brandenkmurray (1)
- chrisakroyd (1)
- GuilhermeFSCampos (1)
- mihaimorariu (1)
- akshaybapat04 (1)
- imparkss (1)
- milani (1)
- hannarud (1)
Top Labels
Issue Labels
enhancement (10)
help wanted (8)
bug (1)
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 11 last-month
- Total docker downloads: 29
- Total dependent packages: 0
- Total dependent repositories: 20
- Total versions: 1
- Total maintainers: 1
pypi.org: keras-rcnn
- Homepage: https://github.com/broadinstitute/keras-rcnn
- Documentation: https://keras-rcnn.readthedocs.io/
- License: MIT
-
Latest release: 0.0.2
published over 8 years ago
Rankings
Stargazers count: 2.7%
Docker downloads count: 3.0%
Dependent repos count: 3.3%
Forks count: 3.4%
Dependent packages count: 7.3%
Average: 8.8%
Downloads: 33.4%
Maintainers (1)
Last synced:
6 months ago
Dependencies
docs/requirements.txt
pypi
- jsonschema *
- keras *
- keras-resnet *
- numpydoc *
- scikit-image *
- sphinx-gallery *
- tensorflow *
setup.py
pypi
- jsonschema >=3.2.0
- keras >=2.3.1
- keras-resnet >=0.2.0
- scikit-image >=0.17.2