chen742/PiPa
Official Implementation of PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
This project helps computer vision practitioners train models to accurately identify and outline objects in real-world images, even when the models were initially trained on synthetic data like video game screenshots. It takes in labeled images from a source domain (e.g., simulated environments) and unlabeled real-world images from a target domain, producing a refined model that can precisely segment objects in the real-world images. This tool is ideal for researchers or engineers working on computer vision tasks who need to deploy models from virtual training environments to actual environments without extensive manual labeling of real-world data.
100 stars. No commits in the last 6 months.
Use this if you need to adapt a semantic segmentation model trained on synthetic, labeled data to perform accurately on unlabeled real-world images.
Not ideal if you already have abundant labeled real-world data for training your semantic segmentation model from scratch.
Stars
100
Forks
15
Language
Python
License
—
Category
Last pushed
Jul 23, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/chen742/PiPa"
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.