Classification models trained on ImageNet. Keras.
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Updated
Jul 21, 2022 - Python
Classification models trained on ImageNet. Keras.
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.
This repository contains the source code of our work on designing efficient CNNs for computer vision
ImageNet file xml format to Darknet text format
ImageNet model implemented using the Keras Functional API
Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support
Identify objects in images using a third-generation deep residual network.
Nearly Perfect & Easily Understandable PyTorch Implementation of SKNet
Multi-label classification based on timm.
React UI for Image object detection using tensorflow.js
PyTorch implementation of DiracDeltaNet from paper Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset
Pytorch based Android app, which classifies images using MobileNet-V2 model, takes image using CameraX API
Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.
Tensorflow Faster R-CNN for Windows and Python 3.5
Image recognition and classification using Convolutional Neural Networks with TensorFlow
Multi-label classification based on timm, and add SimCLR to timm.
Node.js API for Image object detection using tensorflow.js
A deep learning based application which is entitled to help the visually impaired people. The application automatically generates the textual description of what's happening in front of the camera and conveys it to person through audio. It is capable of recognising faces and tell user whether a known person is standing in front of him or not.
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"
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