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

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Segmentation models with pretrained backbones. PyTorch.

Sandbox for training convolutional networks for computer vision

Implementation of EfficientNet model. Keras and TensorFlow Keras.

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

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

Caffe Implementation of Google's MobileNets (v1 and v2)

Classification with PyTorch.

[CVPR2020] GhostNet: More Features from Cheap Operations

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

Classification models trained on ImageNet. Keras.

Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv.org/abs/1806.10779

Evaluation of the CNN design choices performance on ImageNet-2012.

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

PyTorch to Keras model convertor

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

Train the HRNet model on ImageNet

MobileNetV3 in pytorch and ImageNet pretrained models

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

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition

Multi-Scale Dense Networks for Resource Efficient Image Classification （ICLR 2018 Oral）

MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks