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.


  • ObjectDetection - Localizing and identifying multiple objects in a single image.
  • Mask R-CNN - 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.
  • Faster-RCNN - This is an experimental Tensorflow implementation of Faster RCNN - a convnet for object detection with a region proposal network.
  • YOLO TensorFlow - This is tensorflow implementation of the YOLO:Real-Time Object Detection.
  • YOLO TensorFlow ++ - TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices.
  • MobileNet - MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
  • DeepLab - Deep labeling for semantic image segmentation.
  • Colornet - Neural Network to colorize grayscale images.
  • SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.
  • DeepOSM - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery.
  • Domain Transfer Network - Implementation of Unsupervised Cross-Domain Image Generation.
  • Show, Attend and Tell - Attention Based Image Caption Generator.
  • android-yolo - Real-time object detection on Android using the YOLO network, powered by TensorFlow.
  • DCSCN Super Resolution - 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.
  • GAN-CLS - This is an experimental tensorflow implementation of synthesizing images.
  • U-Net - For Brain Tumor Segmentation.
  • Improved CycleGAN -Unpaired Image to Image Translation.
  • Im2txt - Image-to-text neural network for image captioning.
  • Street - Identify the name of a street (in France) from an image using a Deep RNN.
  • SLIM - Image classification models in TF-Slim.
  • DELF - Deep local features for image matching and retrieval.
  • Compression - Compressing and decompressing images using a pre-trained Residual GRU network.
  • AttentionOCR - A model for real-world image text extraction.


  • Mask R-CNN - 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
  • VGG16 - Very Deep Convolutional Networks for Large-Scale Image Recognition.
  • VGG19 - Very Deep Convolutional Networks for Large-Scale Image Recognition.
  • ResNet - Deep Residual Learning for Image Recognition.
  • Image analogies - Generate image analogies using neural matching and blending.
  • Popular Image Segmentation Models - Implementation of Segnet, FCN, UNet and other models in Keras.
  • Ultrasound nerve segmentation - This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation.
  • DeepMask object segmentation - This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks.
  • Monolingual and Multilingual Image Captioning - This is the source code that accompanies Multilingual Image Description with Neural Sequence Models.
  • pix2pix - Keras implementation of Image-to-Image Translation with Conditional Adversarial Networks by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A.
  • Colorful Image colorization - B&W to color.
  • CycleGAN - Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
  • DualGAN - Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.
  • Super-Resolution GAN - Implementation of _Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.


  • FastPhotoStyle - A Closed-form Solution to Photorealistic Image Stylization.
  • pytorch-CycleGAN-and-pix2pix - A Closed-form Solution to Photorealistic Image Stylization.
  • maskrcnn-benchmark - Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
  • deep-image-prior - Image restoration with neural networks but without learning.
  • StarGAN - StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation.
  • faster-rcnn.pytorch - This project is a faster faster R-CNN implementation, aimed to accelerating the training of faster R-CNN object detection models.
  • pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs. - PyTorch
  • Augmentor - Image augmentation library in Python for machine learning. - PyTorch
  • albumentations - Fast image augmentation library.
  • Deep Video Analytics - Deep Video Analytics is a platform for indexing and extracting information from videos and images
  • semantic-segmentation-pytorch - Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset.
  • An End-to-End Trainable Neural Network for Image-based Sequence Recognition - 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.
  • UNIT - PyTorch Implementation of our Coupled VAE-GAN algorithm for Unsupervised Image-to-Image Translation.
  • Neural Sequence labeling model - Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation.
  • faster rcnn - 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-semantic-segmentation - PyTorch for Semantic Segmentation.
  • EDSR-PyTorch - PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
  • image-classification-mobile - Collection of classification models pretrained on the ImageNet-1K.
  • FaderNetworks - Fader Networks: Manipulating Images by Sliding Attributes - NIPS 2017.
  • neuraltalk2-pytorch - Image captioning model in pytorch (finetunable cnn in branch with_finetune).
  • RandWireNN - Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition".
  • stackGAN-v2 |Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++.
  • Detectron models for Object Detection - This code allows to use some of the Detectron models for object detection from Facebook AI Research with PyTorch.
  • 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.
  • pointnet.pytorch - Pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.
  • 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.
  • vnet.pytorch - A Pytorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.
  • piwise - Pixel-wise segmentation on VOC2012 dataset using pytorch.
  • pspnet-pytorch - PyTorch implementation of PSPNet segmentation network.
  • pytorch-SRResNet - Pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.
  • PNASNet.pytorch - PyTorch implementation of PNASNet-5 on ImageNet.
  • imgclassificationpk_pytorch - Quickly comparing your image classification models with the state-of-the-art models.
  • Deep Neural Networks are Easily Fooled - High Confidence Predictions for Unrecognizable Images.
  • pix2pix-pytorch - PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".
  • NVIDIA/semantic-segmentation - A PyTorch Implementation of Improving Semantic Segmentation via Video Propagation and Label Relaxation, In CVPR2019.
  • Neural-IMage-Assessment - A PyTorch Implementation of Neural IMage Assessment.
  • torchxrayvision | Pretrained models for chest X-ray (CXR) pathology predictions. Medical, Healthcare, Radiology - PyTorch

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