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”- [Tensorflow]!(#tensorflow)
- [Keras]!(#keras)
- [PyTorch]!(#pytorch)
- [Caffe]!(#caffe)
- [MXNet]!(#mxnet)
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)