balavenkatesh3322/CV-pretrained-model
A collection of computer vision pre-trained models.
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Computer Vision Pretrained Models
Section titled “Computer Vision Pretrained Models”
What is pre-trained Model?
Section titled “What is pre-trained Model?”A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application.
For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.
Other Pre-trained Models
Section titled “Other Pre-trained Models”Model Deployment library
Section titled “Model Deployment library”Framework
Section titled “Framework”Model visualization
Section titled “Model visualization”You can see visualizations of each model’s network architecture by using Netron.
Tensorflow
Section titled “Tensorflow ”| Model Name | Description | Framework | License |
|---|---|---|---|
| [ObjectDetection]!( https://github.com/tensorflow/models/tree/master/research/object_detection) | Localizing and identifying multiple objects in a single image. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [Mask R-CNN]!( https://github.com/matterport/Mask_RCNN) | The model generates bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone. | Tensorflow | [The MIT License (MIT)]!( https://raw.githubusercontent.com/matterport/Mask_RCNN/master/LICENSE ) |
| [Faster-RCNN]!( https://github.com/smallcorgi/Faster-RCNN_TF) | This is an experimental Tensorflow implementation of Faster RCNN - a convnet for object detection with a region proposal network. | Tensorflow | [MIT License]!( https://raw.githubusercontent.com/smallcorgi/Faster-RCNN_TF/master/LICENSE ) |
| [YOLO TensorFlow]!( https://github.com/gliese581gg/YOLO_tensorflow) | This is tensorflow implementation of the YOLO:Real-Time Object Detection. | Tensorflow | [Custom]!( https://raw.githubusercontent.com/gliese581gg/YOLO_tensorflow/master/LICENSE ) |
| [YOLO TensorFlow ++]!( https://github.com/thtrieu/darkflow) | TensorFlow implementation of ‘YOLO: Real-Time Object Detection’, with training and an actual support for real-time running on mobile devices. | Tensorflow | [GNU GENERAL PUBLIC LICENSE]!( https://raw.githubusercontent.com/thtrieu/darkflow/master/LICENSE ) |
| [MobileNet]!( https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) | MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. | Tensorflow | [The MIT License (MIT)]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [DeepLab]!( https://github.com/tensorflow/models/tree/master/research/deeplab) | Deep labeling for semantic image segmentation. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [Colornet]!( https://github.com/pavelgonchar/colornet) | Neural Network to colorize grayscale images. | Tensorflow | Not Found |
| [SRGAN]!( https://github.com/tensorlayer/srgan) | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. | Tensorflow | Not Found |
| [DeepOSM]!( https://github.com/trailbehind/DeepOSM) | Train TensorFlow neural nets with OpenStreetMap features and satellite imagery. | Tensorflow | [The MIT License (MIT)]!( https://raw.githubusercontent.com/trailbehind/DeepOSM/master/LICENSE ) |
| [Domain Transfer Network]!( https://github.com/yunjey/domain-transfer-network) | Implementation of Unsupervised Cross-Domain Image Generation. | Tensorflow | [MIT License]!( https://raw.githubusercontent.com/yunjey/domain-transfer-network/master/LICENSE ) |
| [Show, Attend and Tell]!( https://github.com/yunjey/show-attend-and-tell) | Attention Based Image Caption Generator. | Tensorflow | [MIT License]!( https://raw.githubusercontent.com/yunjey/show-attend-and-tell/master/LICENSE ) |
| [android-yolo]!( https://github.com/natanielruiz/android-yolo) | Real-time object detection on Android using the YOLO network, powered by TensorFlow. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/natanielruiz/android-yolo/master/LICENSE ) |
| [DCSCN Super Resolution]!( https://github.com/jiny2001/dcscn-super-resolutiont) | This is a tensorflow implementation of “Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network”, a deep learning based Single-Image Super-Resolution (SISR) model. | Tensorflow | Not Found |
| [GAN-CLS]!( https://github.com/zsdonghao/text-to-image) | This is an experimental tensorflow implementation of synthesizing images. | Tensorflow | Not Found |
| [U-Net]!( https://github.com/zsdonghao/u-net-brain-tumor) | For Brain Tumor Segmentation. | Tensorflow | Not Found |
| [Improved CycleGAN]!( https://github.com/luoxier/CycleGAN_Tensorlayer) | Unpaired Image to Image Translation. | Tensorflow | [MIT License]!( https://raw.githubusercontent.com/luoxier/CycleGAN_Tensorlayer/master/LICENSE ) |
| [Im2txt]!( https://github.com/tensorflow/models/tree/master/research/im2txt) | Image-to-text neural network for image captioning. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [SLIM]!( https://github.com/tensorflow/models/tree/master/research/slim) | Image classification models in TF-Slim. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [DELF]!( https://github.com/tensorflow/models/tree/master/research/delf) | Deep local features for image matching and retrieval. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [Compression]!( https://github.com/tensorflow/models/tree/master/research/compression) | Compressing and decompressing images using a pre-trained Residual GRU network. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
| [AttentionOCR]!( https://github.com/tensorflow/models/tree/master/research/attention_ocr) | A model for real-world image text extraction. | Tensorflow | [Apache License]!( https://raw.githubusercontent.com/tensorflow/models/master/LICENSE ) |
Keras
Section titled “Keras ”| Model Name | Description | Framework | License |
|---|---|---|---|
| [Mask R-CNN]!( https://github.com/matterport/Mask_RCNN) | The model generates bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/matterport/Mask_RCNN/master/LICENSE ) |
| [VGG16]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg16.py) | Very Deep Convolutional Networks for Large-Scale Image Recognition. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [VGG19]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg19.py) | Very Deep Convolutional Networks for Large-Scale Image Recognition. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [ResNet]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet_common.py) | Deep Residual Learning for Image Recognition. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| ResNet50 | Deep Residual Learning for Image Recognition. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| Nasnet | NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [MobileNet]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet.py) | MobileNet v1 models for Keras. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [MobileNet V2]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet_v2.py) | MobileNet v2 models for Keras. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [MobileNet V3]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet_v3.py) | MobileNet v3 models for Keras. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [efficientnet]!( https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py) | Rethinking Model Scaling for Convolutional Neural Networks. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/keras-team/keras-applications/master/LICENSE ) |
| [Image analogies]!( https://github.com/awentzonline/image-analogies) | Generate image analogies using neural matching and blending. | Keras | [The MIT License (MIT)]!( https://raw.githubusercontent.com/awentzonline/image-analogies/master/LICENSE.txt ) |
| [Popular Image Segmentation Models]!( https://github.com/divamgupta/image-segmentation-keras) | Implementation of Segnet, FCN, UNet and other models in Keras. | Keras | [MIT License]!( https://raw.githubusercontent.com/divamgupta/image-segmentation-keras/master/LICENSE ) |
| [Ultrasound nerve segmentation]!( https://github.com/jocicmarko/ultrasound-nerve-segmentation) | This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. | Keras | [MIT License]!( https://raw.githubusercontent.com/jocicmarko/ultrasound-nerve-segmentation/master/LICENSE.md ) |
| [DeepMask object segmentation]!( https://github.com/abbypa/NNProject_DeepMask) | This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks. | Keras | Not Found |
| [Monolingual and Multilingual Image Captioning]!( https://github.com/elliottd/GroundedTranslation) | This is the source code that accompanies Multilingual Image Description with Neural Sequence Models. | Keras | [BSD-3-Clause License]!( https://raw.githubusercontent.com/elliottd/GroundedTranslation/master/LICENSE ) |
| [pix2pix]!( https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix) | Keras implementation of Image-to-Image Translation with Conditional Adversarial Networks by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. | Keras | Not Found |
| [Colorful Image colorization]!( https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful) | B&W to color. | Keras | Not Found |
| [CycleGAN]!( https://github.com/eriklindernoren/Keras-GAN/blob/master/cyclegan/cyclegan.py) | Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. | Keras | [MIT License]!( https://raw.githubusercontent.com/eriklindernoren/Keras-GAN/master/LICENSE ) |
| DualGAN | Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation. | Keras | [MIT License]!( https://raw.githubusercontent.com/eriklindernoren/Keras-GAN/master/LICENSE ) |
| [Super-Resolution GAN]!( https://github.com/eriklindernoren/Keras-GAN/blob/master/srgan/srgan.py) | Implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. | Keras | [MIT License]!( https://raw.githubusercontent.com/eriklindernoren/Keras-GAN/master/LICENSE ) |
PyTorch
Section titled “PyTorch ”| Model Name | Description | Framework | License |
|---|---|---|---|
| detectron2 | Detectron2 is Facebook AI Research’s next generation software system that implements state-of-the-art object detection algorithms | PyTorch | Apache License 2.0 |
| [FastPhotoStyle]!( https://github.com/NVIDIA/FastPhotoStyle) | A Closed-form Solution to Photorealistic Image Stylization. | PyTorch | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public Licens]!( https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md ) |
| [pytorch-CycleGAN-and-pix2pix]!( https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) | A Closed-form Solution to Photorealistic Image Stylization. | PyTorch | [BSD License]!( https://raw.githubusercontent.com/junyanz/pytorch-CycleGAN-and-pix2pix/master/LICENSE ) |
| [maskrcnn-benchmark]!( https://github.com/facebookresearch/maskrcnn-benchmark) | Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/facebookresearch/maskrcnn-benchmark/master/LICENSE ) |
| [deep-image-prior]!( https://github.com/DmitryUlyanov/deep-image-prior) | Image restoration with neural networks but without learning. | PyTorch | [Apache License 2.0]!( https://raw.githubusercontent.com/DmitryUlyanov/deep-image-prior/master/LICENSE ) |
| [StarGAN]!( https://github.com/yunjey/StarGAN) | StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/yunjey/StarGAN/master/LICENSE ) |
| [faster-rcnn.pytorch]!( https://github.com/jwyang/faster-rcnn.pytorch) | This project is a faster faster R-CNN implementation, aimed to accelerating the training of faster R-CNN object detection models. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/jwyang/faster-rcnn.pytorch/master/LICENSE ) |
| [pix2pixHD]!( https://github.com/NVIDIA/pix2pixHD) | Synthesizing and manipulating 2048x1024 images with conditional GANs. | PyTorch | [BSD License]!( https://raw.githubusercontent.com/NVIDIA/pix2pixHD/master/LICENSE.txt ) |
| [Augmentor]!( https://github.com/mdbloice/Augmentor) | Image augmentation library in Python for machine learning. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/mdbloice/Augmentor/master/LICENSE.md ) |
| [albumentations]!( https://github.com/albumentations-team/albumentations) | Fast image augmentation library. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/albumentations-team/albumentations/master/LICENSE ) |
| [Deep Video Analytics]!( https://github.com/AKSHAYUBHAT/DeepVideoAnalytics) | Deep Video Analytics is a platform for indexing and extracting information from videos and images | PyTorch | [Custom]!( https://raw.githubusercontent.com/AKSHAYUBHAT/DeepVideoAnalytics/master/LICENSE ) |
| [semantic-segmentation-pytorch]!( https://github.com/CSAILVision/semantic-segmentation-pytorch) | Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset. | PyTorch | [BSD 3-Clause License]!( https://raw.githubusercontent.com/CSAILVision/semantic-segmentation-pytorch/master/LICENSE ) |
| [An End-to-End Trainable Neural Network for Image-based Sequence Recognition]!( https://github.com/bgshih/crnn) | This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR. | PyTorch | [The MIT License (MIT)]!( https://raw.githubusercontent.com/bgshih/crnn/master/LICENSE ) |
| [UNIT]!( https://github.com/mingyuliutw/UNIT) | PyTorch Implementation of our Coupled VAE-GAN algorithm for Unsupervised Image-to-Image Translation. | PyTorch | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License]!( https://raw.githubusercontent.com/mingyuliutw/UNIT/master/LICENSE.md ) |
| [Neural Sequence labeling model]!( https://github.com/jiesutd/NCRFpp) | Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. | PyTorch | [Apache License]!( https://raw.githubusercontent.com/jiesutd/NCRFpp/master/LICENCE ) |
| [faster rcnn]!( https://github.com/longcw/faster_rcnn_pytorch) | This is a PyTorch implementation of Faster RCNN. This project is mainly based on py-faster-rcnn and TFFRCNN. For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/longcw/faster_rcnn_pytorch/master/LICENSE ) |
| [pytorch-semantic-segmentation]!( https://github.com/ZijunDeng/pytorch-semantic-segmentation) | PyTorch for Semantic Segmentation. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/ZijunDeng/pytorch-semantic-segmentation/master/LICENSE ) |
| [EDSR-PyTorch]!( https://github.com/thstkdgus35/EDSR-PyTorch) | PyTorch version of the paper ‘Enhanced Deep Residual Networks for Single Image Super-Resolution’. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/thstkdgus35/EDSR-PyTorch/master/LICENSE ) |
| [image-classification-mobile]!( https://github.com/osmr/imgclsmob) | Collection of classification models pretrained on the ImageNet-1K. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/osmr/imgclsmob/master/LICENSE ) |
| [FaderNetworks]!( https://github.com/facebookresearch/FaderNetworks) | Fader Networks: Manipulating Images by Sliding Attributes - NIPS 2017. | PyTorch | [Creative Commons Attribution-NonCommercial 4.0 International Public License]!( https://raw.githubusercontent.com/facebookresearch/FaderNetworks/master/LICENSE ) |
| [neuraltalk2-pytorch]!( https://github.com/ruotianluo/ImageCaptioning.pytorch) | Image captioning model in pytorch (finetunable cnn in branch with_finetune). | PyTorch | [MIT License]!( https://raw.githubusercontent.com/ruotianluo/ImageCaptioning.pytorch/master/LICENSE ) |
| [RandWireNN]!( https://github.com/seungwonpark/RandWireNN) | Implementation of: “Exploring Randomly Wired Neural Networks for Image Recognition”. | PyTorch | Not Found |
| [stackGAN-v2]!( https://github.com/hanzhanggit/StackGAN-v2) | Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/hanzhanggit/StackGAN-v2/master/LICENSE ) |
| [Detectron models for Object Detection]!( https://github.com/ignacio-rocco/detectorch) | This code allows to use some of the Detectron models for object detection from Facebook AI Research with PyTorch. | PyTorch | [Apache License]!( https://raw.githubusercontent.com/ignacio-rocco/detectorch/master/LICENSE ) |
| [DEXTR-PyTorch]!( https://github.com/scaelles/DEXTR-PyTorch) | This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. | PyTorch | [GNU GENERAL PUBLIC LICENSE]!( https://raw.githubusercontent.com/scaelles/DEXTR-PyTorch/master/LICENSE ) |
| [pointnet.pytorch]!( https://github.com/fxia22/pointnet.pytorch) | Pytorch implementation for “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/fxia22/pointnet.pytorch/master/LICENSE ) |
| [self-critical.pytorch]!( https://github.com/ruotianluo/self-critical.pytorch) | This repository includes the unofficial implementation Self-critical Sequence Training for Image Captioning and Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/ruotianluo/self-critical.pytorch/master/LICENSE ) |
| [vnet.pytorch]!( https://github.com/mattmacy/vnet.pytorch) | A Pytorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. | PyTorch | [BSD 3-Clause License]!( https://raw.githubusercontent.com/mattmacy/vnet.pytorch/master/LICENSE ) |
| [piwise]!( https://github.com/bodokaiser/piwise) | Pixel-wise segmentation on VOC2012 dataset using pytorch. | PyTorch | [BSD 3-Clause License]!( https://raw.githubusercontent.com/bodokaiser/piwise/master/LICENSE.md ) |
| [pspnet-pytorch]!( https://github.com/Lextal/pspnet-pytorch) | PyTorch implementation of PSPNet segmentation network. | PyTorch | Not Found |
| [pytorch-SRResNet]!( https://github.com/twtygqyy/pytorch-SRResNet) | Pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. | PyTorch | [The MIT License (MIT)]!( https://raw.githubusercontent.com/twtygqyy/pytorch-SRResNet/master/LICENSE ) |
| [PNASNet.pytorch]!( https://github.com/chenxi116/PNASNet.pytorch) | PyTorch implementation of PNASNet-5 on ImageNet. | PyTorch | [Apache License]!( https://raw.githubusercontent.com/chenxi116/PNASNet.pytorch/master/LICENSE ) |
| [img_classification_pk_pytorch]!( https://github.com/felixgwu/img_classification_pk_pytorch) | Quickly comparing your image classification models with the state-of-the-art models. | PyTorch | Not Found |
| [Deep Neural Networks are Easily Fooled]!( https://github.com/utkuozbulak/pytorch-cnn-adversarial-attacks) | High Confidence Predictions for Unrecognizable Images. | PyTorch | [MIT License]!( https://raw.githubusercontent.com/utkuozbulak/pytorch-cnn-adversarial-attacks/master/LICENSE ) |
| [pix2pix-pytorch]!( https://github.com/mrzhu-cool/pix2pix-pytorch) | PyTorch implementation of “Image-to-Image Translation Using Conditional Adversarial Networks”. | PyTorch | Not Found |
| [NVIDIA/semantic-segmentation]!( https://github.com/NVIDIA/semantic-segmentation) | A PyTorch Implementation of Improving Semantic Segmentation via Video Propagation and Label Relaxation, In CVPR2019. | PyTorch | [CC BY-NC-SA 4.0 license]!( https://raw.githubusercontent.com/NVIDIA/semantic-segmentation/master/LICENSE ) |
| [Neural-IMage-Assessment]!( https://github.com/kentsyx/Neural-IMage-Assessment) | A PyTorch Implementation of Neural IMage Assessment. | PyTorch | Not Found |
| torchxrayvision | Pretrained models for chest X-ray (CXR) pathology predictions. Medical, Healthcare, Radiology | PyTorch | [Apache License]!( https://raw.githubusercontent.com/mlmed/torchxrayvision/master/LICENSE ) |
| pytorch-image-models | PyTorch image models, scripts, pretrained weights — (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2, MNASNet, Single-Path NAS, FBNet, and more | PyTorch | [Apache License 2.0]!( https://github.com/rwightman/pytorch-image-models/blob/master/LICENSE ) |
Caffe
Section titled “Caffe ”| Model Name | Description | Framework | License |
|---|---|---|---|
| [OpenPose]!( https://github.com/CMU-Perceptual-Computing-Lab/openpose) | OpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 130 keypoints) on single images. | Caffe | [Custom]!( https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/LICENSE ) |
| [Fully Convolutional Networks for Semantic Segmentation]!( https://github.com/shelhamer/fcn.berkeleyvision.org) | Fully Convolutional Models for Semantic Segmentation. | Caffe | Not Found |
| [Colorful Image Colorization]!( https://github.com/richzhang/colorization) | Colorful Image Colorization. | Caffe | [BSD-2-Clause License]!( https://raw.githubusercontent.com/richzhang/colorization/master/LICENSE ) |
| [R-FCN]!( https://github.com/YuwenXiong/py-R-FCN) | R-FCN: Object Detection via Region-based Fully Convolutional Networks. | Caffe | [MIT License]!( https://raw.githubusercontent.com/YuwenXiong/py-R-FCN/master/LICENSE ) |
| [cnn-vis]!( https://github.com/jcjohnson/cnn-vis) | Inspired by Google’s recent Inceptionism blog post, cnn-vis is an open-source tool that lets you use convolutional neural networks to generate images. | Caffe | [The MIT License (MIT)]!( https://raw.githubusercontent.com/jcjohnson/cnn-vis/master/LICENSE ) |
| [DeconvNet]!( https://github.com/HyeonwooNoh/DeconvNet) | Learning Deconvolution Network for Semantic Segmentation. | Caffe | [Custom]!( https://raw.githubusercontent.com/HyeonwooNoh/DeconvNet/master/LICENSE ) |
MXNet
Section titled “MXNet ”| Model Name | Description | Framework | License |
|---|---|---|---|
| [Faster RCNN]!( https://github.com/ijkguo/mx-rcnn) | Region Proposal Network solves object detection as a regression problem. | MXNet | [Apache License, Version 2.0]!( https://raw.githubusercontent.com/ijkguo/mx-rcnn/master/LICENSE ) |
| [SSD]!( https://github.com/zhreshold/mxnet-ssd) | SSD is an unified framework for object detection with a single network. | MXNet | [MIT License]!( https://raw.githubusercontent.com/zhreshold/mxnet-ssd/master/LICENSE ) |
| [Faster RCNN+Focal Loss]!( https://github.com/unsky/focal-loss) | The code is unofficial version for focal loss for Dense Object Detection. | MXNet | Not Found |
| [CNN-LSTM-CTC]!( https://github.com/oyxhust/CNN-LSTM-CTC-text-recognition) | I realize three different models for text recognition, and all of them consist of CTC loss layer to realize no segmentation for text images. | MXNet | Not Found |
| [Faster_RCNN_for_DOTA]!( https://github.com/jessemelpolio/Faster_RCNN_for_DOTA) | This is the official repo of paper DOTA: A Large-scale Dataset for Object Detection in Aerial Images. | MXNet | [Apache License]!( https://raw.githubusercontent.com/jessemelpolio/Faster_RCNN_for_DOTA/master/LICENSE ) |
| [RetinaNet]!( https://github.com/unsky/RetinaNet) | Focal loss for Dense Object Detection. | MXNet | Not Found |
| [MobileNetV2]!( https://github.com/liangfu/mxnet-mobilenet-v2) | This is a MXNet implementation of MobileNetV2 architecture as described in the paper Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. | MXNet | [Apache License]!( https://raw.githubusercontent.com/liangfu/mxnet-mobilenet-v2/master/LICENSE ) |
| [neuron-selectivity-transfer]!( https://github.com/TuSimple/neuron-selectivity-transfer) | This code is a re-implementation of the imagenet classification experiments in the paper Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. | MXNet | [Apache License]!( https://raw.githubusercontent.com/TuSimple/neuron-selectivity-transfer/master/LICENSE ) |
| [MobileNetV2]!( https://github.com/chinakook/MobileNetV2.mxnet) | This is a Gluon implementation of MobileNetV2 architecture as described in the paper Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. | MXNet | [Apache License]!( https://raw.githubusercontent.com/chinakook/MobileNetV2.mxnet/master/LICENSE ) |
| [sparse-structure-selection]!( https://github.com/TuSimple/sparse-structure-selection) | This code is a re-implementation of the imagenet classification experiments in the paper Data-Driven Sparse Structure Selection for Deep Neural Networks. | MXNet | [Apache License]!( https://raw.githubusercontent.com/TuSimple/sparse-structure-selection/master/LICENSE ) |
| [FastPhotoStyle]!( https://github.com/NVIDIA/FastPhotoStyle) | A Closed-form Solution to Photorealistic Image Stylization. | MXNet | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License]!( https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md ) |
Contributions
Section titled “Contributions”Your contributions are always welcome!!
Please have a look at contributing.md
License
Section titled “License”[MIT License]!(LICENSE)