| # 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") |