Pinned Repositories
api-design
LivingSocial API Design Guide
cocoapi
COCO API - Dataset @ http://cocodataset.org/
discolight
discolight is a robust, flexible and infinitely hackable library for generating image augmentations ✨
models
Models and examples built with TensorFlow
mongodb-memory-server
Spinning up mongod in memory for fast tests. If you run tests in parallel this lib helps to spin up dedicated mongodb servers for every test file in MacOS, *nix, Windows or CI environments (in most cases with zero-config).
python-pylint-github-action
Github Action for Pylint with Python Slim image
scp-action
GitHub Action that copy files and artifacts via SSH.
ssh-action
GitHub Actions for executing remote ssh commands.
tensorflow-onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
denzel-datature's Repositories
denzel-datature/api-design
LivingSocial API Design Guide
denzel-datature/cocoapi
COCO API - Dataset @ http://cocodataset.org/
denzel-datature/discolight
discolight is a robust, flexible and infinitely hackable library for generating image augmentations ✨
denzel-datature/models
Models and examples built with TensorFlow
denzel-datature/mongodb-memory-server
Spinning up mongod in memory for fast tests. If you run tests in parallel this lib helps to spin up dedicated mongodb servers for every test file in MacOS, *nix, Windows or CI environments (in most cases with zero-config).
denzel-datature/python-pylint-github-action
Github Action for Pylint with Python Slim image
denzel-datature/scp-action
GitHub Action that copy files and artifacts via SSH.
denzel-datature/ssh-action
GitHub Actions for executing remote ssh commands.
denzel-datature/tensorflow-onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
denzel-datature/tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
denzel-datature/tf-faster-rcnn
Tensorflow 2 Faster-RCNN implementation from scratch supporting to the batch processing with MobileNetV2 and VGG16 backbones