convolutional-networks

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Differentiable architecture search for convolutional and recurrent networks

Image Deblurring using Generative Adversarial Networks

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Paper Lists for Graph Neural Networks

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

CNN visualization tool in TensorFlow

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

Evaluation of the CNN design choices performance on ImageNet-2012.

A self driving toy car using end-to-end learning

Fully Convlutional Neural Networks for state-of-the-art time series classification

U-Net: Convolutional Networks for Biomedical Image Segmentation

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

Keras tutorial for beginners (using TF backend)

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

Curated Tensorflow code resources to help you get started with Deep Learning.

Outdated, see new https://github.com/braindecode/braindecode

Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

PyTorch implementation of several SSD based object detection algorithms.

Grayscale Image Colorization with Generative Adversarial Networks. https://arxiv.org/abs/1803.05400

Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

Naszilla is a Python library for neural architecture search (NAS)

A playable implementation of Fully Convolutional Networks with Keras.

PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks

Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"

High-quality Neural Networks for Computer Vision 😎