covisionlab/diffusion_labeling
Official implementation of "Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map" accepted at Synthetic Data for Computer Vision Workshop - CVPR 2025
This project helps quality control engineers and manufacturing professionals create synthetic industrial images and their corresponding defect maps. It takes simple bounding box annotations (e.g., specifying where a scratch or dent should be) and generates realistic images of industrial products, along with precise segmentation masks highlighting those 'defects'. This allows for the creation of vast, varied datasets needed to train automated defect detection systems without needing real faulty products.
No commits in the last 6 months.
Use this if you need to generate high-quality synthetic images of industrial products with specific, localized defects and their precise segmentation maps, especially for training computer vision models.
Not ideal if you're looking for a simple, out-of-the-box solution that doesn't require technical setup or if your primary need is general image generation without specific defect localization guidance.
Stars
10
Forks
1
Language
Python
License
—
Category
Last pushed
Aug 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/covisionlab/diffusion_labeling"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cswry/SeeSR
[CVPR2024] SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
JJLibra/SALAD-Pan
🤗 Official implementation for "SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for...
open-mmlab/mmgeneration
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.
Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
hanjq17/Spectrum
[CVPR 2026] Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration