Update README.md

README.md

@@ -1,3 +1,58 @@
----
-license: cc
----
+---
+license: cc
+---
+## The files in the dataset are organized as follows: 
+1) ***seed_to_sim_determinisitic***
+2) ***sim_to_exp_diffusion***
+
+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. 
+
+1) ***seed_to_sim_determinisitic***
+
+- `Sim_050924_seed.tar` is the input seed dataset, `Sim_050924_intermediate_Tp3.tar` is the output, default end-point patterns and `Sim_050924_complex_Tp3.tar` is the end-point patterns with different parameters- thinner but denser branching. 
+- `Sim_050924_ModelTesting_seed.tar` is the input seed test dataset, `Sim_050924_ModelTesting_intermediate.tar` is the default patterns for the test set, and `Sim_050924_ModelTesting_complex.tar` is the thinner but denser branches test set. 
+`saved_models.tar` contains all saved trained models that are used in the manuscript. 
+
+    i)`Pixel_32x32x3to32x32x4_dilRESNET_30k_graypatterns_seedtointermediate_v101_4-1759366230_best.pt` is the model used in Fig 2 for mapping between seed to simulation. 
+
+    ii)`Pixel_32x32x3to32x32x4_dilRESNET_graypatterns_intermediatetocomplex_Model_30000_v101_Cluster_GPU_tfData-1759363890_best.pt` is the model used in Fig 3 to map between one simulation to another. 
+
+    iii) `models_Fig4` contain all models that were used in Fig4a and b- testing the model performance as a function of training data size. The number following intermediatetocomplex and preceeding _v1015 represents the training data size. 
+
+    iv) `models_dataagumentation_Fig4` contains all models that were used in Fig4c and d- testing the model performance as a function of unique training data size. The number following intermediatetocomplex and preceeding _v1015 represents the unique training data size(Total training size used for all images was 40k, different images represents different amounts of augmentation accordingly)
+
+2) ***sim_to_exp_diffusion***
+
+- `Exp.tar` contains the raw experimental images that are used in the model training.
+- `Exp_SimcorrtoExp_seed.tar` contains the seeding configurations of the experimental images in the training set. 
+- `SimcorrtoExp.tar` contains the paired simulation images corresponding to the experimental dataset. 
+- `Exp_testset.tar` contains the experimental images that are used in the model inference as ground truths 
+- `Exp_SimcorrtoExp_testset_seed.tar` contains the seeding configurations corresponding to the experimental and simulation images in the test set. 
+- `SimcorrtoExp_testset.tar` contain the paired simulatoin images that are used in the model inference as spatial inputs. 
+- `checkpoint_simtoexp.tar` is the trained ControlNet model checkpoint used in Fig 5 to map from simulation to experiments.
+- `checkpoint_seedtoexp.tar` is the trained ControlNet model checkpoint used in Supplementary Fig 17 to map from seed to experiments. 
+- `inference_images.tar` contains various results from the trained ControlNet model on the test set. 
+
+    i) `v2025926_1251_simtoexp_v3` contains the results of the base ControlNet model used in Fig 5.
+
+    ii) `v20251011_841_seedtoexp_swapped_v3` contains the results of the ControlNet trained on seeding 
+    configurations as spatial input in Supp Fig 17. 
+    The rest of the images are from the ablation study shown in Supp Fig 18. 
+
+    iii) `v20251023_1458_no_guess` : Guess mode= True
+
+    iv) `v20251023_1753_no_negative`: Blank negative prompt
+
+    v) `v20251023_1756_plus_positive`: Added positive prompt
+
+    vi) `v20251023_1758_low_strength_point85`: Lower conditioning control
+
+    vii) `v20251023_1758_high_strength_1point25`: Higher conditioning control
+
+    viii) `v20251023_1759_higher_DDIM_steps_100`: Higher DDIM steps(100)
+    
+    ix) `v20251023_181_lower_guidance_9point0`: Lower guidance scale of 9.0 used in model training
+
+    
+
+