convolutional-networks

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

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

Differentiable architecture search for convolutional and recurrent networks

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

Image Deblurring using Generative Adversarial Networks

Paper Lists for Graph Neural Networks

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

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

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).

Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

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

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

CNN visualization tool in TensorFlow

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

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

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)

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

High-quality Neural Networks for Computer Vision 😎

U-Net: Convolutional Networks for Biomedical Image Segmentation