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cfc2391 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # 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") |