Update README.md

README.md

@@ -56,98 +56,162 @@ Using the model output by the learning mechanism and the binary file (.dat) outp
 
 Original source code exist in https://github.com/mina98/TEZip-Libtorch-Main.git 
 
-Setting Up the Environment
+# Neural Compression Model
 
-Before running the model, set up the necessary environment variables:
+## Model Description
 
--export PATH=/home/mwahba/cuda-12.1/bin${PATH:+:${PATH}}
--export LD_LIBRARY_PATH=/home/mwahba/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+This model implements neural compression using ConvLSTM and PredNet architectures for efficient video/image data compression and decompression.
 
-Creating Training Data
-To create training data, use the following command:
+## Environment Setup
 
-./build/main train_data_create train_data_dir val_data_dir(optional) save_dir
-
-Replace:
-
-train_data_dir with the path to your training data
-val_data_dir (optional) with the path to your validation data
-save_dir with the directory where the processed data should be saved.
-
-Run example
+### Prerequisites
+- CUDA 12.1
+- LibTorch
+- C++ build environment
 
-./build/main train_data_create 2011_09_26/2011_09_26_drive_0027_sync/ data
-
-## How to Get Started with the Model
+### Environment Variables
+Set up the following environment variables before running:
 
-./build/main train_model model_dir data_dir save_dir --verbose --model model_name
+```bash
+export PATH=/home/mwahba/cuda-12.1/bin${PATH:+:${PATH}}
+export LD_LIBRARY_PATH=/home/mwahba/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+```
 
-Replace:
+## Usage
 
-model_dir with the path to the directory containing the model
-data_dir with the dataset path
-save_dir with the directory where the trained model should be saved
-model_name with the specific model architecture to be used (e.g., convlstm).
-The --verbose flag enables detailed logging.
+### 1. Creating Training Data
 
-Run example
+**Command:**
+```bash
+./build/main train_data_create train_data_dir val_data_dir(optional) save_dir
+```
 
-./build/main tezip --learn model/ data/  --verbose --model convlstm
+**Parameters:**
+- `train_data_dir`: Path to your training data
+- `val_data_dir`: (Optional) Path to your validation data  
+- `save_dir`: Directory where processed data should be saved
 
-Compressing Data
-To compress data using the trained model, use the following command:
+**Example:**
+```bash
+./build/main train_data_create 2011_09_26/2011_09_26_drive_0027_sync/ data
+```
 
-./build/main compress model_dir data_dir save_dir --preprocess preprocess_level --window window_size --verbose --mode mode_type --model model_name --bound bound_value
+### 2. Training the Model
 
-Replace:
+**Command:**
+```bash
+./build/main train_model model_dir data_dir save_dir --verbose --model model_name
+```
 
-model_dir with the path to the trained model,
-data_dir with the directory containing the data to be compressed,
-save_dir with the directory where compressed data will be saved,
-preprocess_level with the preprocessing level (e.g., 3),
-window_size with the window size for processing (e.g., 5),
-mode_type with the compression mode (e.g., pwrel),
-model_name with the model type (e.g., convlstm and prednet),
-bound_value with the compression bound (e.g., 0.000001).
+**Parameters:**
+- `model_dir`: Path to directory containing the model
+- `data_dir`: Dataset path
+- `save_dir`: Directory where trained model should be saved
+- `model_name`: Model architecture (e.g., `convlstm`, `prednet`)
+- `--verbose`: Enables detailed logging
 
-./build/main tezip--compress model_convlstm 2011_09_26/2011_09_26_drive_0027_sync/image_02 comp_0.1/ --preprocess 3 --window 5 --verbose --mode pwrel --model convlstm --bound 0.1 
+**Example:**
+```bash
+./build/main tezip --learn model/ data/ --verbose --model convlstm
+```
 
-Decompressing Data
+### 3. Data Compression
 
-To decompress data using the trained model, use the following command:
+**Command:**
+```bash
+./build/main compress model_dir data_dir save_dir --preprocess preprocess_level --window window_size --verbose --mode mode_type --model model_name --bound bound_value
+```
+
+**Parameters:**
+- `model_dir`: Path to the trained model
+- `data_dir`: Directory containing data to be compressed
+- `save_dir`: Directory where compressed data will be saved
+- `preprocess_level`: Preprocessing level (e.g., `3`)
+- `window_size`: Window size for processing (e.g., `5`)
+- `mode_type`: Compression mode (e.g., `pwrel`)
+- `model_name`: Model type (`convlstm`, `prednet`)
+- `bound_value`: Compression bound (e.g., `0.000001`)
+
+**Example:**
+```bash
+./build/main tezip --compress model_convlstm 2011_09_26/2011_09_26_drive_0027_sync/image_02 comp_0.1/ --preprocess 3 --window 5 --verbose --mode pwrel --model convlstm --bound 0.1
+```
+
+### 4. Data Decompression
+
+**Command:**
+```bash
 ./build/main decompress model_dir compressed_data_dir save_dir --verbose --model model_name
+```
 
-Replace:
-
-model_dir with the path to the trained model,
-compressed_data_dir with the directory containing the compressed data,
-save_dir with the directory where decompressed data will be saved,
-model_name with the model type (e.g., convlstm).
-
-The --verbose flag enables detailed logging of the decompression process.
-
-Run example
+**Parameters:**
+- `model_dir`: Path to the trained model
+- `compressed_data_dir`: Directory containing compressed data
+- `save_dir`: Directory where decompressed data will be saved
+- `model_name`: Model type (e.g., `convlstm`)
+- `--verbose`: Enables detailed logging
 
+**Example:**
+```bash
 ./build/main tezip --uncompress model_convlstm comp_0.1/ decomp_0.1 --verbose --model convlstm
-
-Below is a revised breakdown of the project's file structure:
-
-compress.cpp: Implements the compression functionality.
-decompress.cpp: Implements the decompression functionality.
-the_data.cpp: Generates sequences for LibTorch models.
-conv_lstm_cell.cpp: Defines the ConvLSTM cell used in the ConvLSTM model.
-convlstm.cpp: Defines the ConvLSTM layer used in the ConvLSTM model.
-seq2seq.cpp: Creates an instance of a ConvLSTM model.
-train_convlstm.cpp: Contains the training function for the ConvLSTM model.
-convlstmcell.cpp: Implements the ConvLSTM cell for PredNet.
-prednet.cpp: Implements the PredNet definition.
-tezip.cpp: Acts as the master module, integrating training, compression, and decompression.
-train_data_create.cpp: Generates hickle data for training.
-train.cpp: Contains the training function for the PredNet model.
-manual_data_loader.cpp: Simulates the data loader functionality.
-main.cpp: Serves as the master file to run both the train_data_create and tezip modules.
-
-This structure clearly outlines the responsibilities of each file in the project.
+```
+
+## Model Architecture
+
+### Supported Models
+- **ConvLSTM**: Convolutional LSTM for spatiotemporal sequence modeling
+- **PredNet**: Predictive coding network for video prediction
+
+### Key Features
+- Neural compression and decompression
+- Support for video/image sequences
+- Configurable preprocessing levels
+- Adjustable compression bounds
+- Window-based processing
+
+## File Structure
+
+### Core Components
+- **`main.cpp`**: Master file for running train_data_create and tezip modules
+- **`tezip.cpp`**: Master module integrating training, compression, and decompression
+- **`train_data_create.cpp`**: Generates hickle data for training
+
+### Model Implementation
+- **`convlstm.cpp`**: ConvLSTM layer implementation
+- **`conv_lstm_cell.cpp`**: ConvLSTM cell definition
+- **`seq2seq.cpp`**: ConvLSTM model instance creation
+- **`train_convlstm.cpp`**: ConvLSTM training function
+- **`prednet.cpp`**: PredNet model implementation
+- **`convlstmcell.cpp`**: ConvLSTM cell for PredNet
+- **`train.cpp`**: PredNet training function
+
+### Data Processing
+- **`the_data.cpp`**: Sequence generation for LibTorch models
+- **`manual_data_loader.cpp`**: Data loader functionality simulation
+- **`compress.cpp`**: Compression functionality implementation
+- **`decompress.cpp`**: Decompression functionality implementation
+
+## Technical Requirements
+
+### Dependencies
+- CUDA 12.1
+- LibTorch
+- C++ compiler with C++14 support or higher
+
+### Hardware Requirements
+- NVIDIA GPU with CUDA support
+- Sufficient GPU memory for model training and inference
+
+## Performance
+
+### Compression Efficiency
+- Configurable compression bounds (e.g., 0.1, 0.000001)
+- Adaptive preprocessing levels
+- Window-based processing for memory efficiency
+
+### Processing Modes
+- **pwrel**: Pixel-wise relative compression mode
+- Additional modes may be available depending on implementation
 
 ## Evaluation