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README.md

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+# 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")