Update README.md

README.md

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 # Brain-Diffuser
 Official repository for the paper ["**Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion**"](https://arxiv.org/abs/2303.05334) by Furkan Ozcelik and Rufin VanRullen.
 
-## Results
-The following are a few reconstructions obtained : 
-<p align="center"><img src="./figures/Reconstructions.png" width="600" ></p>
 
 ## Instructions 
 
@@ -43,27 +40,12 @@ wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-asse
 ### Second Stage Reconstruction with Versatile Diffusion
 
 1. Download pretrained Versatile Diffusion model "vd-four-flow-v1-0-fp16-deprecated.pth", "kl-f8.pth" and "optimus-vae.pth" from [HuggingFace](https://huggingface.co/shi-labs/versatile-diffusion/tree/main/pretrained_pth) and put them in `versatile_diffusion/pretrained/` folder
-2. Extract CLIP-Text features of captions for any subject 'x' using `python scripts/cliptext_extract_features.py -sub x`
+<!-- 2. Extract CLIP-Text features of captions for any subject 'x' using `python scripts/cliptext_extract_features.py -sub x` -->
 3. Extract CLIP-Vision features of stimuli images for any subject 'x' using `python scripts/clipvision_extract_features.py -sub x`
-4. Train regression models from fMRI to CLIP-Text features and save test predictions using `python scripts/cliptext_regression.py -sub x`
+<!-- 4. Train regression models from fMRI to CLIP-Text features and save test predictions using `python scripts/cliptext_regression.py -sub x` --> -->
 5. Train regression models from fMRI to CLIP-Vision features and save test predictions using `python scripts/clipvision_regression.py -sub x`
 6. Reconstruct images from predicted test features using `python scripts/versatilediffusion_reconstruct_images.py -sub x` . This code is written as you are using two 12GB GPUs but you may edit according to your setup. 
 
-
-### Quantitative Evaluation
-Although results are expected to be similar, it may vary because of variations at reconstruction
-1. Save test images to directory `python scripts/save_test_images.py`
-2. Extract evaluation features for test images using `python scripts/eval_extract_features.py -sub 0`
-3. Extract evaluation features for reconstructed images of any subject using `python scripts/eval_extract_features.py -sub x`
-4. Obtain quantitative metric results for each subject using`python scripts/evaluate_reconstruction.py -sub x`
-
-### ROI Analysis
-It has a bug that prevents to get the exact results but provides an approximation for most of ROIs, hopefully will be fixed soon.
-1. Extract ROI fMRI activations for any subject 'x' using `python scripts/roi_extract.py -sub x`
-2. Generate VDVAE, CLIP-Text, CLIP-Vision features forom synthetic fMRI using `python scripts/roi_generate_features.py -sub x`
-3. Generate VDVAE reconstructions for ROIs using `python scripts/roi_vdvae_reconstruct.py -sub x`
-4. Generate Versatile Diffusion reconstructions for ROIs using `python scripts/roi_versatilediffusion_reconstruct.py -sub x`
-
 ## References
 - Codes in vdvae directory are derived from [openai/vdvae](https://github.com/openai/vdvae)
 - Codes in versatile_diffusion directory are derived from earlier version of [SHI-Labs/Versatile-Diffusion](https://github.com/SHI-Labs/Versatile-Diffusion)