imagenet-classifier

Classification models trained on ImageNet. Keras.

This repository contains the source code of our work on designing efficient CNNs for computer vision

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.

ImageNet file xml format to Darknet text format

Multi-label classification based on timm.

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.

Nearly Perfect & Easily Understandable PyTorch Implementation of SKNet

Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support

ImageNet model implemented using the Keras Functional API

ImageNet model implemented using the Keras Functional API

Multi-label classification based on timm, and add SimCLR to timm.

A codebase & model zoo for pretrained backbone based on MegEngine.

A codebase & model zoo for pretrained backbone based on MegEngine.

PyTorch implementation of DiracDeltaNet from paper Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Identify objects in images using a third-generation deep residual network.

Identify objects in images using a third-generation deep residual network.

Pytorch based Android app, which classifies images using MobileNet-V2 model, takes image using CameraX API