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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.

convolutional-networks deep-learning yolo2 realtime regressionReal-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.

tensorflow graph darknet deep-learning deep-neural-networks convolutional-neural-networks convolutional-networks image-processing object-detection machine-learning real-time mobile-developmentDARTS: 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.

deep-learning automl image-classification language-modeling pytorch convolutional-networks recurrent-networks neural-architecture-searchFigure 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.

tensorflow tensorboard convolutional-neural-networks cnn visualization deepdream convolutional-networksPyTorch implementation of Fully Convolutional Networks. See VOC example.

pytorch computer-vision deep-learning semantic-segmentation convolutional-networks fcn fcn8sWelcome 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.

convolutional-neural-networks convolutional-networks batch-size caffenet lr-policy architecture relu caffe benchmark activations dataset imagenetTo 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.

raspberry-pi cnn-keras deep-learning machine-learning servo self-driving-car convolutional-networksThis 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.

image-segmentation keras medical-imaging deep-neural-networks convolutional-networksNote: The old braindecode repository has been moved to https://github.com/robintibor/braindevel. A deep learning toolbox to decode raw time-domain EEG.

deep-learning eeg brain-signal-decoding convolutional-networks convolutional-neural-networksCN24 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.

convolutional-networks opencl deep-learning segmentationDeep Object Tracking Implementation in Tensorflow for 'Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning(CVPR 2017)'

deep-learning deep-neural-networks computer-vision object-tracking object-tracker tracking tracker reinforcement-learning convolutional-neural-networks convolutional-networks cnn tensorflowA 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.

machine-learning dropout lstm mnist lenet neural-turing-machines question-answering computational-graphs auto-differentiation convolutional-neural-networks convolutional-networks recurrent-neural-networks lstm-model deep-learning deep-q-network reinforcement-learning cartpoleIn the Jupyter notebook for this repository, I begin by calculating the bottleneck features for the CIFAR-10 dataset. These features are then visualized with a Barnes-Hut implementation of t-SNE, which is the fastest t-SNE implementation to date.

keras keras-neural-networks keras-models keras-classification-models keras-visualization images image-classification classification classifier classification-algorithm cnn cnn-keras cnn-model cnn-architecture convolutional-neural-networks convolutional-networks tsne tsne-algorithm visualization transfer-learningChainer 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.

computer-vision chainer deep-learning segmentation fcn convolutional-networks semantic-segmentation fcn8sCNN-based affine shape estimator.

deep-learning local-features convolutional-neural-networks convolutional-networks pytorch computer-vision hessian image-retrieval image-matching affine-shape-estimatorThis 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.

keras lsuv lsuv-initialization convolutional-neural-networks convolutional-networks deeplearning initializationThis 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.

pytorch lsuv lsuv-initialization convolutional-neural-networks convolutional-networks deeplearning initialization cnnMishkin, D. and Matas, J.,(2015). All you need is a good init. arXiv preprint arXiv:1511.06422.

caffe lsuv lsuv-initialization initialization convolutional-neural-networks convolutional-networks deep-learningThis is a PyTorch implementation of the GeniePath model in <GeniePath: Graph Neural Networks with Adaptive Receptive Paths> (https://arxiv.org/abs/1802.00910)

convolutional-networks gcn pyg gat gnn graph-network graph-neural-network
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