Popular GitHub Repositories for Coco Ssd Mobilenet

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

OpenMMLab Detection Toolbox and Benchmark

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Single Shot MultiBox Detector in TensorFlow

OpenMMLab Pre-training Toolbox and Benchmark

Tensorflow Faster RCNN for Object Detection

SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection

Mask RCNN in TensorFlow

The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(https://arxiv.org/abs/1804.06208)"

???YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.

Convolutional Neural Networks to predict the aesthetic and technical quality of images.

Implementation of EfficientNet model. Keras and TensorFlow Keras.

TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO

Building a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow

MobileNet build with Tensorflow