Update Readme.md with information encoding decoding part added too

README.md

@@ -4,8 +4,9 @@ license: cc
 ## The files in the dataset are organized as follows: 
 1) ***seed_to_sim_determinisitic***
 2) ***sim_to_exp_diffusion***
+3) ***information_encoding_decoding***
 
-These two folders represent the 2 major pipelines in the manuscript: Physics constrained photorealistic prediction of bacterial colony patterns (Insert link later). First folder is for the determinisitic ResNet model and second folder is for the diffusion model. 
+These three folders represent the 3 major pipelines in the manuscript: Mechanistically conditioned generative modelling of biologically realistic bacterial patterns (Insert link later). First folder is for the determinisitic ResNet model, second folder is for the diffusion model, and third is for testing the inverse problem prediction with generated patterns. 
 
 1) ***seed_to_sim_determinisitic***
 
@@ -55,6 +56,15 @@ These two folders represent the 2 major pipelines in the manuscript: Physics con
     ix) `v20251023_181_lower_guidance_9point0`: Lower guidance scale of 9.0 used in model training
 
     
+3) ***information_encoding_decoding***
+
+- `Exp_testset_seed.tar` contains the experimental seed images(random seeding locations) that are used in the model testset. 
+- `Exp_testset_fixed.tar` contains the experimental image with defined seeding conditions that are used in the testset. 
+- `Exp_testset_fixed_seed.tar` contains the defined seeding conditions that are used in the testset. 
+- `Generated_patterns_selected.tar` contains a select few synthetic generated patterns for display in Fig 6. 
+- `checkpoint_exptoseed.tar` is the trained UNet model checkpoint used in Fig 6 to map from experiment to seeds
+- `checkpoint_simtoseed.tar` is the trained UNet model checkpoint used in Supplementary Fig 20 to map from simulations/experiments to seeds. 
+
 
     
 Note: 
@@ -62,7 +72,7 @@ Note:
 
 2) Supplementary Figure 13 in the manuscipt involves the use of experimental images. To run this model, the appropriate images can be downloaded from the sim_to_exp_diffusion dataset.
 
-   
+3) For synthetic data generation used in Figure 6, entire library is quite big (30GB), can be generated using script in Github repo. Selected images for display are attached in `Generated_patterns_selected.tar`