1088 Open source projects that are alternatives of or similar to super-gradients

Segmentation models with pretrained backbones. PyTorch.

Sandbox for training deep learning networks

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

PyTorch implementation of "Pyramid Scene Parsing Network".

Address book for computer vision models.

Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

Tensorflow implementation of "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network"

Implementation of EfficientNet model. Keras and TensorFlow Keras.

Powerful and efficient Computer Vision Annotation Tool (CVAT)

PyTorch-based modular, configuration-driven framework for knowledge distillation. 🏆18 methods including SOTA are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy.

EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

PiGallery: AI-powered Self-hosted Secure Multi-user Image Gallery and Detailed Image analysis using Machine Learning, EXIF Parsing and Geo Tagging

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.

72.8% MobileNetV2 1.0 model on ImageNet and a spectrum of pre-trained MobileNetV2 models

Implementation code of the paper: FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction, NeurIPS 2018

A light-weight, power efficient, and general purpose convolutional neural network

OpenMMLab Image Classification Toolbox and Benchmark

Repository to identify Lego bricks automatically only using images

Listing my favorite research papers 📝 from different fields as I read them.

[Paper 2020] Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training. Plus, an image classification toolbox includes ResNet, Wide-ResNet, ResNeXt, ResNeSt, ResNeXSt, SENet, Shake-Shake, DenseNet, PyramidNet, and EfficientNet.

Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper.

CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

Train the HRNet model on ImageNet

PSPNet in Chainer

Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search

Pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"

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

35% faster than ResNet: Harmonic DenseNet, A low memory traffic network

Programming Assignments and Lectures for Stanford's CS 231: Convolutional Neural Networks for Visual Recognition

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.

Classification models trained on ImageNet. Keras.

Trial on kaggle imagenet object localization by yolo v3 in google cloud

🍊 🎑 Orange3 add-on for dealing with image related tasks

[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions

Mobilenet V2(Inverted Residual) Implementation & Trained Weights Using Tensorflow

Labelbox is the fastest way to annotate data to build and ship computer vision applications.

A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)

implement AlexNet with C / convolutional nerual network / machine learning / computer vision

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the…

[ICLR2022] official implementation of UniFormer

[IEEE Access] "Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-constrained Edge Computing Systems" and [ACM MobiCom HotEdgeVideo 2019] "Distilled Split Deep Neural Networks for Edge-assisted Real-time Systems"

Paper notes in deep learning/machine learning and computer vision

An Out-of-the-Box Replication of GANimation using PyTorch, pretrained weights are available!

🤖 Cozmo the Robot recognizes objects with TensorFlow

Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition"

73.6% GhostNet 1.0x pre-trained model on ImageNet

A list of popular deep learning models related to classification, segmentation and detection problems

🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation. 🍞🍖🍕

Bottleneck Transformers for Visual Recognition

Parametric Contrastive Learning (ICCV2021)

No description or website provided.

Protein Language Model

ImageNet pre-trained SharpPeleeNet can be used in real-time Semantic Segmentation/Objects Detection

PyTorch-style and human-readable RegNet with a spectrum of pre-trained models