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augmentor

Image augmentation library in Python for machine learning.

caffe

Caffe: a fast framework for deep learning. For the most recent version checkout the dev branch. For the latest stable release checkout the master branch.

crf-rnn_tensorflow

Tensorflow implementation of CRF-RNN

deepgaze

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking.

docker_gems

face_age_gender_emotion

Training on multiple datasets with ''mixed batches'' strategy

fastsceneunderstanding

segmentation, instance segmentation and single image depth

fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015.

glsl-projectron

WebGL demo to evolve data that projects into a given target image

gpu_memory_profiling

Profile the GPU memory usage of every line in a Pytorch code

gpustat

📊 A simple command-line utility for querying and monitoring GPU status

gtsrb.torch

Traffic sign recognition with Torch

handy_scripts

happei

ifan

Integrated Face Analytics Network

imgaug

Image augmentation for machine learning experiments.

keras_intro

latex_gist

line_profiler

Line-by-line profiling for Python

line_profiler_example

mhp2_eval

Evaluation for MHP_v2 dataset

mhpm

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

protobuf

Protocol Buffers - Google's data interchange format

python-progressbar

Text progress bar library for Python

pytorch-deeplab-resnet

DeepLab resnet model in pytorch

setgpu

Small Python library to automatically set CUDA_VISIBLE_DEVICES to the least loaded device on multi-GPU systems.

tensorflow-deeplab-resnet

DeepLab-ResNet rebuilt in TensorFlow

tensorflow_gems

Some useful scripts when using Tensorflow

tf_train_with_generator_in_background_queue

This is an example code to realize the following functions: Train Tensorflow model with feed_dict. The data are generated with a generator. A Queue is used to run the generator in separate threads.