Displaying 1 to 20 from 21 results

CV-pretrained-model - A collection of computer vision pre-trained models.

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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.

transformers - 🤗Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX

  •    Python

🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

PaddleClas - A treasure chest for visual recognition powered by PaddlePaddle

  •    Python

PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks. Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition.




mmf - A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

  •    Python

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. See full list of project inside or built on MMF here. MMF is powered by PyTorch, allows distributed training and is un-opinionated, scalable and fast. Use MMF to bootstrap for your next vision and language multimodal research project by following the installation instructions. Take a look at list of MMF features here.

tensornets - High level network definitions with pre-trained weights in TensorFlow

  •    Python

High level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT

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Pre-trained models for human pose estimation capable of running in real time on Jetson Nano. This makes it easy to detect features like left_eye, left_elbow, right_ankle, etc. Training scripts to train on any keypoint task data in MSCOCO format. This means you can experiment with training trt_pose for keypoint detection tasks other than human pose.

cnn-models - ImageNet pre-trained models with batch normalization for the Caffe framework

  •    Python

This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.


functional-zoo - PyTorch and Tensorflow functional model definitions

  •    Jupyter

PyTorch, unlike lua torch, has autograd in it's core, so using modular structure of torch.nn modules is not necessary, one can easily allocate needed Variables and write a function that utilizes them, which is sometimes more convenient. This repo contains model definitions in this functional way, with pretrained weights for some models. Weights are serialized as a dict of arrays in hdf5, so should be easily loadable in other frameworks. Thanks to @edgarriba we have cpp_parser for loading weights in C++.

pytorch-cnn-finetune - Fine-tune pretrained Convolutional Neural Networks with PyTorch

  •    Python

VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers. See examples/cifar10.py file (requires PyTorch 0.4).

finetuner - Finetuning any DNN for better embedding on neural search tasks

  •    Python

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks. It accompanies Jina to deliver the last mile of performance for domain-specific neural search applications. 🎛 Designed for finetuning: a human-in-the-loop deep learning tool for leveling up your pretrained models in domain-specific neural search applications.

gensim-data - Data repository for pretrained NLP models and NLP corpora.

  •    Python

Research datasets regularly disappear, change over time, become obsolete or come without a sane implementation to handle the data format reading and processing. For this reason, Gensim launched its own dataset storage, committed to long-term support, a sane standardized usage API and focused on datasets for unstructured text processing (no images or audio). This Gensim-data repository serves as that storage.

mdm - A TensorFlow implementation of the Mnemonic Descent Method.

  •    Jupyter

A Tensorflow implementation of the Mnemonic Descent Method. We are an avid supporter of the Menpo project (http://www.menpo.org/) which we use in various ways throughout the implementation.

BIRADS_classifier - High-resolution breast cancer screening with multi-view deep convolutional neural networks

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This is an implementation of the model used for BI-RADS classification as described in our paper "High-resolution breast cancer screening with multi-view deep convolutional neural networks". The implementation allows users to get the BI-RADS prediction by applying our pretrained CNN model on standard screening mammogram exam with four views. As a part of this repository, we provide a sample exam (in images directory). The model is implemented in both TensorFlow and PyTorch. To use the pretrained model, the input is required to consist of four images, one for each view (L-CC, L-MLO, R-CC, R-MLO). Each image has to have the size of 2600x2000 pixels. The images in the provided sample exam were already cropped to the correct size.

breast_density_classifier - Breast density classification with deep convolutional neural networks

  •    Python

This is an implementation of the model used for breast density classification as described in our paper "Breast density classification with deep convolutional neural networks". The implementation allows users to get breast density predictions by applying one of our pretrained models: a histogram-based model or a multi-view CNN. Both models act on screening mammography exams with four standard views. As a part of this repository, we provide a sample exam (in images directory). The models are implemented in both TensorFlow and PyTorch. To use one of the pretrained models, the input is required to consist of four images, one for each view (L-CC, L-MLO, R-CC, R-MLO). Each image has to have the size of 2600x2000 pixels. The images in the provided sample exam were already cropped to the correct size.

Open-Source-Models - Address book for computer vision models.

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Open-Source-Models is a archive for all the open source computer vision models. Training Computer Vision models is an arduous task which involves a series of strenuous tasks such as collecting the images, annotating them, uploading them on cloud(In case you don't have a rig with a beffy GPU) and training them for hours and hours (which also requires you to find a workaround so that the colab doesn't timeout). All the steps mentioned above are to be executed without making any error as a small oversight can lead to a model trained with faulty config file, incorrect annotations etc. Thanks to all the generous people in the field of computer vision which are doing all the above tasks and providing thier work to others as an open source project, so that not everyone has to reinvent neural networks and can focus on the actual task that has to be carried out with the model. This archive consists of models with different architecture, accuracy, and framework in the same category as different use cases demand different types of model to achieve similar goals.

huggingface_hub - Client library to download and publish models and other files on the huggingface

  •    Python

This library allows anyone to work with the Hub repositories: you can clone them, create them and upload your models to them. On top of this, the library also offers methods to access information from the Hub. For example, listing all models that meet specific criteria or get all the files from a specific repo. You can find the library implementation here. We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations here.

rl-trained-agents - A collection of pre-trained RL agents using Stable Baselines3

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Pre-trained Reinforcement Learning agents using the rl-baselines3-zoo and Stable Baselines3. This repository contains only the trained policies, please look at the zoo for the training scripts.

flaxmodels - Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

  •    Python

The goal of this project is to make current deep learning models more easily available for the awesome Jax/Flax ecosystem. You will need Python 3.7 or later.

x-lxmert - PyTorch code for EMNLP 2020 paper "X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers"

  •    Python

Recent multi-modal transformers have achieved tate of the art performance on a variety of multimodal discriminative tasks like visual question answering and generative tasks like image captioning. This begs an interesting question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family - LXMERT - finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements. X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Please checkout ./feature_extraction for download pre-extracted features and more details.






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