wasidennis/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
This project helps computer vision researchers adapt existing semantic segmentation models, trained on synthetic images (like GTA5), to perform accurately on real-world images (like Cityscapes). You input pre-trained models and image datasets from both synthetic and real environments, and it outputs a more robust segmentation model capable of accurately identifying objects in real-world scenes. This is for researchers in computer vision or autonomous driving who need to improve model performance across different visual domains without extensive real-world labeling.
861 stars. No commits in the last 6 months.
Use this if you need to improve the accuracy of semantic segmentation models on real-world image datasets, especially when your initial training data is synthetic and you want to avoid costly real-world data labeling.
Not ideal if you are looking for an out-of-the-box solution for general image classification or object detection, or if you don't have access to both synthetic and real-world image datasets for domain adaptation.
Stars
861
Forks
207
Language
Python
License
—
Category
Last pushed
Aug 14, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/wasidennis/AdaptSegNet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepinv/deepinv
DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning
yjxiong/tsn-pytorch
Temporal Segment Networks (TSN) in PyTorch
mhamilton723/STEGO
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
fidler-lab/polyrnn-pp
Inference Code for Polygon-RNN++ (CVPR 2018)
pyxu-org/pyxu
Modular and scalable computational imaging in Python with GPU/out-of-core computing.