There are 28 repositories under convolutional-networks topic.
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
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
Image Deblurring using Generative Adversarial 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
Paper Lists for Graph Neural 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).
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
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.
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
Fully Convlutional Neural Networks for state-of-the-art time series classification
A self driving toy car using end-to-end learning
A framework that provides a simple API for developing ML-driven data processing and search pipelines.
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)
U-Net: Convolutional Networks for Biomedical Image Segmentation
Curated Tensorflow code resources to help you get started with Deep Learning.
Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available
Outdated, see new https://github.com/braindecode/braindecode
High-quality Neural Networks for Computer Vision 😎
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
Naszilla is a Python library for neural architecture search (NAS)
PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks
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.)
Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"
A playable implementation of Fully Convolutional Networks with Keras.
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer
Spatial Pyramid Network for Optical Flow
Implemented and improved the iTracker model proposed in the paper "Eye Tracking for Everyone"
Repository has been moved: https://github.com/adobe/antialiased-cnns