A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection. - Releases · HsLOL/RetinaNet-PyTorch
Pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming ...
Aug 11, 2019 · pytorch-retinanet Pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár. This implementation is primarily designed to be easy to read and simple to modify. Results
A PyTorch implementation of RetinaNet with `ResNet` backbone - GitHub - benihime91/pytorch_retinanet: A PyTorch implementation of RetinaNet with `ResNet` ...
$ git clone https://github.com/benihime91/pytorch_retinanet.git For easy training pipeline, we recommend using pytorch-lightning for training and testing. First of all open the hparams.yaml file and modify it according to need. Instructions to modeify the same are present inside the file. Create a python script inside the retinanet repo.
Download Custom Dataset. Write Training Configuation yaml file . Train Detection Model . Use Trained PyTorch RetinaNet Object Detection For Inference on Test ...
11.08.2019 · Pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár. This implementation is primarily designed to be easy to read and simple to modify. Currently, this repo achieves 33.7%
Aug 20, 2021 · pytorch-retinanet Pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár. This implementation is primarily designed to be easy to read and simple to modify. Results
$ git clone https://github.com/benihime91/pytorch_retinanet.git For easy training pipeline, we recommend using pytorch-lightning for training and testing. First of all open the hparams.yaml file and modify it according to need. Instructions to modeify the same are present inside the file. Create a python script inside the retinanet repo.
Nov 17, 2020 · pytorch-retinanet This repository is an extenstion of the original repository pytorch-retinanet. New features: Batched NMS for faster evaluation Automatic Mixed Precision (AMP) training Distributed training DataParallel (DP) Distributed Data Parallel LARC (borrowed from apex) Augmentations Flip Rotate Shear Brightness Contrast Gamma Saturation