Deep Learning Library for Single Cell Analysis
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Published by willgraf over 3 years ago
This release updates TensorFlow to 2.4.1 which drops support for Python 3.5.x.
ScaleDetection
and LabelDetection
models. (#491)SemanticMovieIterator
to prevent saving duplicate images for every batch. (#492)SiameseDataGenerator
and siamese_model
for multichannel data. (#495)in_shape
argument for Location
layers. (#497)deepcell-toolbox
to 0.9.x and deepcell-tracking
to 0.3.x. (#500)tensorflow
version to 2.4.1, drop support for Python 3.5. (#476)RetinaMask
models to deepcell-retinamask
. (#486)Published by willgraf over 3 years ago
deepcell-toolbox
to <0.9.0 to prevent breaking changes with multiplex_utils.py
.deepcell-tracking
to <0.4.0.Published by willgraf over 3 years ago
deepcell-cpu
deployment script. (#485)Published by willgraf over 3 years ago
deepcell.__version__
. (#482)deepcell.applications
, fix NuclearSegmentation
preprocessing, and update CytoplasmSegmentation
with TensorFlow 2 SavedModel
. (#483)Published by willgraf over 3 years ago
MultiplexSegmentation
model (#467)deepcell-cpu
on new releases (#472)Application._resize_output
handles lists of tensors and single tensor outputs (#468)Published by willgraf almost 4 years ago
opencv
to opencv-headless
to remove GUI dependencies. (#466)deepcell-toolbox
to 0.8.3.deepcell-tracking
to 0.2.7.Published by willgraf almost 4 years ago
NuclearSegmentation
model file and preprocessing.num_semantic_heads
from PanopticNet
to prevent any conflicts with num_semantic_classes
.use_pretrained_weights
from Application
docstrings.Published by willgraf almost 4 years ago
README.md
in the Docker image. (#462)Published by willgraf almost 4 years ago
This release supports TensorFlow 2.3.1+ and drops support for Python 2.7.
pip install deepcell
is now supported. (#461)Applications
load SavedModels
and are decoupled from the current model architecture. (#460)training.py
from all reference notebooks (#458).dtype
bug for disc
transforms (#442).Published by willgraf almost 4 years ago
This will be the final release that supports Python 2.7 and TensorFlow 1.X.
keras.utils.custom_object_scope
and keras.testing_utils.layer_test
for testing layers. (#447)deepcell.applications.MultiplexSegmentation
. (#434, #452)whole_image
normalization. (#446)Published by willgraf about 4 years ago
CroppingDataGenerator
to randomly crop form the source images. (#413)SemanticDataGenerator
supports multiple labels. (#344)MultiplexSegmentation
application with new weights and post-processing (#407, #413, #422, #424, #425, #429, #431)SemanticMovieDataGenerator
for 3D data. (#419)compute_overlap
to deepcell_toolbox
. (#409)pixelwise
transform. (#415)PanopticNet
model for 3D data. (#416)Published by willgraf over 4 years ago
This release supports TensorFlow 1.14.x and 1.15.x as well as python 2.7 and 3.6+.
Updated match_nodes
to return IoU directly instead of indices. (#267)
Added get_anchor_parameters
to automatically determine feature pyramid parameters (#269)
Added new custom layer, ConvGRU2D
(#278)
Updated layer_test
testing routine (#279)
Travis will now tag and push a latest-gpu
image with every release. (#281)
Speed up the pixel-wise transform (#286, #295)
Add temporal information options to the featurenets. (#282)
Improve docstrings for sphinx compatibility. (#310)
Improved data quality for cytoplasm and phase data. (#318)
Added new model_zoo.PanopticNet
and SemanticDataGenerator
to generalize a model for learning multiple tasks simultaneously, both regression and classification. (#319)
Added Application
objects to easily use models with a simple API. (#341)
Simplified transform names (#376).
deepcell-tracking
has been updated to 0.2.4, which resolves some ISBI function bugs. (#267)
Updated tf.image.resize_images
to tf.image.resize
as the former is deprecated. (#268)
Correct upper limit for clipping boxes (#277)
Fixed broken data links and README links (#292, #300, #307)
Migrate general utility functions into a new package deepcell-toolbox
(#319).
Fixed RetinaNet interpolation bug (#357)
Support for TensorFlow 1.10.x - 1.13.x has been dropped.
The default TF_VERSION
in the Dockerfile has been updated to 1.14.0-gpu
, as many users were expecting this. (#281, #311)
MaskRCNN
has been refactored to RetinaMask
(#360).
/scripts
has been migrated to /notebooks
(#374).
deepcell.notebooks
has been removed (#390).
Published by willgraf almost 5 years ago
This release fully supports Tensorflow 1.10.x through 1.14.x, and Python 2.7, 3.5, 3.6. Future releases will drop support for TensorFlow 1.10.x, 1.11.x 1.12.x, 1.13.x as well as dropped support for Python 3.5.
Replaces tracking.py
and tracking_utils.py
with a dependency on the pip package deepcell_tracking
. (PR #254)
Break image_generators.py
into a submodule with each family of generators in a different file. (PR #258)
Updated reshape_matrix
to work with non-square matrices. (PR #257)
Fixed namespace imports. (PR #262)
3D FeatureNet models can now be re-instantiated with a new frames_per_batch value. (PR #250)
ImageNormalization
layers to not have trainable weights (PR #250), which unfortunately means that models trained in versions <0.4.0 cannot be loaded with version 0.4.0+.Published by willgraf almost 5 years ago
frames_per_batch
was added to both RetinaNet
and RetinaMask
which have both been adapted for 3D data if frames_per_batch
is greater than 1.
Other bugs were fixed:
deepcell.model_zoo
and deepcell.applications
were also significantly improved by parameterizing tests instead of using a for-loop.Published by willgraf about 5 years ago
Compatible with TensorFlow versions 1.10+, this release forms the basis for the cell tracking and benchmarking algorithms put forth in the publication "Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning" (https://doi.org/10.1101/803205).
Published by willgraf about 5 years ago
As we strive to support the latest versions of TensorFlow, we are forced to deprecate certain older versions of TensorFlow. This release of deepcell-tf supports TensorFlow 1.8+, however, all further development will be built for TensorFlow versions 1.10+.