README.md

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license: cc
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## 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.
- `Dissimilarity_scoring.tar` contains the images used in Supplementary Figures 17 and 18, and the trained contrastive learning model. 
- `inference_folders.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

    

    
Note: 
1) The datasets in the manuscript are augmented using rotations to increase the training size for model training. All the datasets here are non-augmented. Instructions on how to augment the dataset are outlined in the github repo. 

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.