Displaying 1 to 18 from 18 results

keras-yolo2 - Easy training on custom dataset

  •    Jupyter

This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend. It supports training YOLOv2 network with various backends such as MobileNet and InceptionV3. Links to demo applications are shown below. Check out https://experiencor.github.io/yolo_demo/demo.html for a Raccoon Detector demo run entirely in brower with DeepLearn.js and MobileNet backend (it somehow breaks in Window). Source code of this demo is located at https://git.io/vF7vG.

darkflow - Translate darknet to tensorflow

  •    Python

Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.

darts - Differentiable architecture search for convolutional and recurrent networks

  •    Python

DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arXiv:1806.09055. NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.

tf_cnnvis - CNN visualization tool in TensorFlow

  •    Python

Figure 1: Original image and the reconstructed versions from maxpool layer 1,2 and 3 of Alexnet generated using tf_cnnvis. The function to generate the activation visualizations of the input image at the given layer.

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

  •    Jupyter

Welcome to evaluation of CNN design choises performance on ImageNet-2012. Here you can find prototxt's of tested nets and full train logs. **upd2.: Some of the pretrained models are in Releases section. They are licensed for unrestricted use.

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

  •    Jupyter

To make a lane follower based on a standard RC car using Raspberry Pi and a camera. The software is a simple Convolutional Network, which takes in the image fetched from the camera and outputs the steering angle. During data collection, we will simply hook the steering PWM of the car to pin GPIO17. The script raspberry_pi/collect_data.py will record the values of steering PWM and the associated images. The data of each trial are collectively stored in driving_trial_*. The trial folders are automatically numbered.

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation. This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

braindecode - A deep learning toolbox to decode raw time-domain EEG.

  •    Python

Note: The old braindecode repository has been moved to https://github.com/robintibor/braindevel. A deep learning toolbox to decode raw time-domain EEG.

cn24 - Convolutional (Patch) Networks for Semantic Segmentation

  •    C++

CN24 is a complete semantic segmentation framework using fully convolutional networks. It supports a wide variety of platforms (Linux, Mac OS X and Windows) and libraries (OpenCL, Intel MKL, AMD ACML...) while providing dependency-free reference implementations. The software is developed in the Computer Vision Group at the University of Jena. The repository contains pre-trained networks for these two applications, which are ready to use.

essence - AutoDiff DAG constructor, built on numpy and Cython

  •    Python

A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

fcn - Chainer Implementation of Fully Convolutional Networks

  •    Python

Chainer implementation of Fully Convolutional Networks. The accuracy of original implementation is computed with (evaluate.py) after converting the caffe model to chainer one using convert_caffe_to_chainermodel.py. You can download vgg16 model from here: vgg16_from_caffe.npz.

LSUV-keras - Simple implementation of the LSUV initialization in keras

  •    Python

This is sample code for LSUV and initializations, implemented in python script within Keras framework. Mishkin, D. and Matas, J.,(2015). All you need is a good init. ICLR 2016 arXiv:1511.06422.

LSUV-pytorch - Simple implementation of the LSUV initialization in PyTorch

  •    Python

This is sample code for LSUV and initializations, implemented in python script within PyTorch framework. Mishkin, D. and Matas, J.,(2015). All you need is a good init. ICLR 2016 arXiv:1511.06422.

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