hynnsk/PPAP
This is the official pytorch implementation of "Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation" (ECCV 2024).
This project helps machine learning engineers and researchers automatically categorize pixels in images without needing manually labeled training data. You provide raw image datasets, and the system processes them to output segmented images where each pixel is assigned to a semantic category (like 'sky', 'road', or 'person'). This is ideal for those working on computer vision applications who need to segment large volumes of images but lack the resources for extensive manual annotation.
No commits in the last 6 months.
Use this if you need to perform semantic segmentation on images but want to avoid the time-consuming and labor-intensive process of manual image labeling.
Not ideal if you require segmentation based on highly specific or rare categories that are not well-represented by pre-trained models or if you need pixel-perfect accuracy for safety-critical applications.
Stars
18
Forks
—
Language
Python
License
MIT
Category
Last pushed
Aug 07, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/hynnsk/PPAP"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
hkchengrex/CascadePSP
[CVPR 2020] CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global...
nv-tlabs/GSCNN
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)
lim-anggun/FgSegNet
FgSegNet: Foreground Segmentation Network, Foreground Segmentation Using Convolutional Neural...
jiwoon-ahn/irn
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)
suyukun666/UFO
Official PyTorch implementation of the “A Unified Transformer Framework for Co-Segmentation,...