one-stage and two-stage detectors and segmentation-based detectors
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Updated
May 2, 2020 - Jupyter Notebook
one-stage and two-stage detectors and segmentation-based detectors
All my Python code used for the Kaggle HuBMAP Semantic Segmentation competition
Pytorch Implementation of Dynamic FPN, PAN, and Bi-FPN
Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.
R2CNN: Rotational Region CNN Based on FPN (Tensorflow)
A deep learning project using SOLO to detect cells through a Biomedical video
Brain MRI segmentation using segmentation models
Segmentation models with pretrained backbones. PyTorch. for Google Colab cell motility segmentation example.
This projects uses video feeds from endoscopic procedures to identify polyps in the gastrointestinal tract and draw masks around them to aid doctors in identifying precursors of colorectal cancer.
Pytorch implementation of dynamic FPN, PAN, and Bi-FPN
Efficient model for semantic segmentation on edge devices, specifically targeting the analysis of disaster scenes from images captured by unmanned aerial vehicles (UAVs).
This is my Pytorch implementation of Segmenting Objects by Locations (SOLO) for instance segmentation
This repository contains our work on ensembling in semantic image segmentation as part of the Google Research Kaggle competition "Identify Contrails to Reduce Global Warming".
Using Faster R-CNN with ResNet50 and FPN to detect abnormalities in mammograms
Brief comparison of segmentation models. Trained on Collab, served using Fast-API, streamlit and dockerized.
Model ensemble: ResNet + FPN, and Focal Loss in TensorFlow2
基于 torchvision 编写的针对 COCO 数据集的 Faster-RCNN 快速训练与推理框架,支持多 batchsize。
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