Update README.md

README.md

@@ -96,54 +96,73 @@ For each dataset type, we also provide pretrained generative model weights:
 
 ---
 
-## 📥 Download Examples
+### Pretrained Weights (.pt)
 
-### Using Python
-```python
-from huggingface_hub import hf_hub_download
+We provide three pretrained weight packs aligned with the dataset families:
 
-# Download sample dataset
-hf_hub_download(
-    repo_id="BGLab/microgen3D",
-    filename="data/experimental.tar.gz",
-    repo_type="dataset",
-    local_dir=""
-)
 
-# Download experimental pretrained weights
-import os
-weights_local_dir = ""
-os.makedirs(weights_local_dir, exist_ok=True)
+- `vae.pt` — Variational Autoencoder weights
+- `fp.pt` — Feature Predictor weights
+- `ddpm.pt` — Latent Diffusion Model weights
 
-files = {
-    "weights/experimental/vae.pt": "vae.pt",
-    "weights/experimental/fp.pt": "fp.pt",
-    "weights/experimental/ddpm.pt": "ddpm.pt"
-}
+### Model/Weights Summary
 
-for remote_path, local_name in files.items():
-    downloaded_path = hf_hub_download(
-        repo_id="BGLab/microgen3D",
-        filename=remote_path,
-        repo_type="dataset"
-    )
-```
+| Pack          | Input shape     | VAE latent size | FP input (flattened) | FP output size (# predicted attrs) | Conditioning params | Manufacturing params | DDPM max features (`n_feat`) |
+|--------------:|:----------------|:----------------|----------------------:|:-----------------------------------:|:-------------------:|:--------------------:|:----------------------------:|
+| CH 2-Phase    | `1,128,128,64`  | `4,8,8,4`       | `1024`                | `7`                                 | `3`                 | `0`                  | `512`                        |
+| CH 3-Phase    | `1,128,128,64`  | `4,8,8,4`       | `1024`                | `7`                                 | `4`                 | `3`                  | `512`                        |
+| Experimental  | `64,64,64`      | `1,8,8,8`       | `512`                 | `3`                                 | `3`                 | `0`                  | `512`                        |
+
+
+To learn more about the attributes and their meanings, see this [link](https://owodolab.github.io/graspi/listOfDescriptors.html).
 
 ## 📁 Repository Structure
 
 ```
 microgen3D/
 ├── data/
-│   └── sample_data.h5                 # Experimental or synthetic HDF5 microstructure file
+│   ├── experimental.tar.gz
+│   ├── ch_2phase.tar.gz
+│   ├── ch_3phase.tar.gz
+│   ├── sample_CH_two_phase.tar.gz
+│   ├── sample_CH_three_phase.tar.gz
+│   ├── experimental/                 # after extracting experimental.tar.gz
+│   │   ├── dataset_info.txt
+│   │   ├── train.h5
+│   │   ├── val.h5
+│   │   └── sample_train.h5
+│   ├── ch_2phase/                    # after extracting ch_2phase.tar.gz
+│   │   ├── dataset_info.txt
+│   │   ├── train/                    # training split (HDF5 shards/files)
+│   │   └── val/                      # validation split
+│   ├── ch_3phase/                    # after extracting ch_3phase.tar.gz
+│   │   ├── dataset_info.txt
+│   │   ├── train/
+│   │   └── val/
+│   ├── ch_2phase_sample/             # after extracting sample_CH_two_phase.tar.gz
+│   │   ├── dataset_info.txt
+│   │   ├── train/
+│   │   └── val/
+│   └── ch_3phase_sample/             # after extracting sample_CH_three_phase.tar.gz
+│       ├── dataset_info.txt
+│       ├── train/
+│       └── val/
 ├── models/
 │   └── weights/
 │       ├── experimental/
-│       │   ├── vae.ckpt
-│       │   ├── fp.ckpt
-│       │   └── ddpm.ckpt
-│       ├── two_phase/
-│       └── three_phase/
+│       │   ├── vae.pt
+│       │   ├── fp.pt
+│       │   └── ddpm.pt
+│       ├── ch_2phase/
+│       │   ├── vae.pt
+│       │   ├── fp.pt
+│       │   └── ddpm.pt
+│       └── ch_3phase/
+│           ├── vae.pt
+│           ├── fp.pt
+│           └── ddpm.pt
 └── ...
+
 ```
 
 ---
@@ -168,70 +187,41 @@ pip install -r requirements.txt
 # Make sure HF CLI is installed and you're logged in: `huggingface-cli login`
 ```
 
+
+## 📥 Download Examples
+
+### Using Python
 ```python
 from huggingface_hub import hf_hub_download
+import os
+
+# Download sample dataset
+hf_hub_download(
+    repo_id="BGLab/microgen3D",
+    filename="data/experimental.tar.gz",   # correct remote path
+    repo_type="dataset",
+    local_dir=""
+)
 
-# Download sample data
-hf_hub_download(repo_id="BGLab/microgen3D", filename="sample_data.h5", repo_type="dataset", local_dir="data")
+# Download experimental pretrained weights
 
-# Download model weights
-hf_hub_download(repo_id="BGLab/microgen3D", filename="vae.ckpt", local_dir="models/weights/experimental")
-hf_hub_download(repo_id="BGLab/microgen3D", filename="fp.ckpt", local_dir="models/weights/experimental")
-hf_hub_download(repo_id="BGLab/microgen3D", filename="ddpm.ckpt", local_dir="models/weights/experimental")
+for fname in ["weights/experimental/vae.pt",
+              "weights/experimental/fp.pt",
+              "weights/experimental/ddpm.pt"]:
+    hf_hub_download(
+        repo_id="BGLab/microgen3D",
+        filename=fname,                # correct remote path
+        repo_type="dataset",
+        local_dir=""
+    )
 ```
-
-## ⚙️ Configuration
-
-### Training Config (`config.yaml`)
-- **task**: Auto-generated if left null  
-- **data_path**: Path to training dataset (`../data/sample_train.h5`)  
-- **model_dir**: Directory to save model weights  
-- **batch_size**: Batch size for training  
-- **image_shape**: Shape of the 3D images `[C, D, H, W]`  
-
-#### VAE Settings:
-- `latent_dim_channels`: Latent space channels size.  
-- `kld_loss_weight`: Weight of KL divergence loss  
-- `max_epochs`: Training epochs  
-- `pretrained`: Whether to use pretrained VAE  
-- `pretrained_path`: Path to pretrained VAE model  
-
-#### FP Settings:
-- `dropout`: Dropout rate  
-- `max_epochs`: Training epochs  
-- `pretrained`: Whether to use pretrained FP  
-- `pretrained_path`: Path to pretrained FP model  
-
-#### DDPM Settings:
-- `timesteps`: Number of diffusion timesteps  
-- `n_feat`: Number of feature channels for Unet. Higher the channels more model capacity. 
-- `learning_rate`: Learning rate  
-- `max_epochs`: Training epochs  
-
-### Inference Parameters (`params.yaml`)
-- **data_path**: Path to inference/test dataset (`../data/sample_test.h5`)  
-
-#### Training (for model init only):
-- `batch_size`, `num_batches`, `num_timesteps`, `learning_rate`, `max_epochs`  : Optional parameters
-
-#### Model:
-- `latent_dim_channels`: Latent space channels size.  
-- `n_feat`: Number of feature channels for Unet.
-- `image_shape`: Expected image input shape  
-
-#### Attributes:
-- List of features/targets to predict:
-  - `ABS_f_D`
-  - `CT_f_D_tort1`
-  - `CT_f_A_tort1`
-
-#### Paths:
-- `ddpm_path`: Path to trained DDPM model  
-- `vae_path`: Path to trained VAE model  
-- `fc_path`: Path to trained FP model  
-- `output_dir`: Where to store inference results  
+### 📂 Extract Dataset
+```bash 
+tar -xzvf data/experimental.tar.gz -C data/ 
+``` 
 
 ## 🏋️ Training
+For inference details refer to the GitHub repository README. [![Code](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/baskargroup/MicroGen3D)
 
 Navigate to the training folder and run:
 ```bash
@@ -240,6 +230,7 @@ python training.py
 ```
 
 ## 🧠 Inference
+For inference details refer to the GitHub repository README. [![Code](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/baskargroup/MicroGen3D)
 
 After training, switch to the inference folder and run:
 ```bash
@@ -269,4 +260,3 @@ If you use this dataset or models, please cite:
 This project is licensed under the **MIT License**.
 
 ---
-