Repository navigation
convolutional-networks
- Website
- Wikipedia
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