# DiffuseSeg Weights

Used for reproducing results of "Label-Efficient Semantic Segmentation with Diffusion Models" (ICLR 2022)

This repository contains weights and features extracted from a Denoising Diffusion Probabilistic Model (DDPM) for segmentation tasks. 
DDPM Weights can be found at "https://huggingface.co/Harish-JHR/DDPM_CelebAHQ64".
Following the approach in the referenced paper, we extract **pixel-level features** from the UpBlock layers of the DDPM and train lightweight segmentation heads on them.

## Contents
- `ddpm_pixel_features_train.pt`: Pixel-level feature vectors from DDPM.
- `ddpm_pixel_labels_train.pt`: Corresponding integer pixel labels for training.
- `mlp_X_best.pt`: Trained MLP segmentation heads (10 in total).

Each MLP corresponds to a segmentation head trained on different layers/features.

## Usage
```python
import torch

features = torch.load("ddpm_pixel_features_train.pt")
labels = torch.load("ddpm_pixel_labels_train.pt")
mlp1 = torch.load("mlp_1_best.pt")