Update README.md

Author: indrapaul824

README.md

@@ -17,7 +17,7 @@
 
 1. [Problem Statement](#problem-statement)
 2. [Data Source](#data-source)
-3. [Model Architecture](#model-architecture)
+3. [Model Architecture](#system-architecture)
 4. [Training and Evaluation](#training-and-evaluation)
 5. [Deployment](#deployment)
 6. [Contributing](#contributing)
@@ -71,14 +71,113 @@ Accurately estimating the intensity of tropical cyclones is important for weathe
 
 The INSAT-3D satellite provides infrared images of the Indian subcontinent that are used as the input to the VORTEX model. The model is trained on a large dataset of historical tropical cyclone images and corresponding intensity estimates.
 
-# Model Architecture
+```bash
+├── ./data
+│   ├── ./data/labels-date-ref.txt
+│   ├── ./data/processed
+│   │   ├── ./data/processed/crop_data
+│   │   │   ├── ./data/processed/crop_data/EXTREME
+│   │   │   ├── ./data/processed/crop_data/NORMAL
+│   │   │   ├── ./data/processed/crop_data/SEVERE
+│   │   │   ├── ./data/processed/crop_data/SUPER
+│   │   │   └── ./data/processed/crop_data/VERY
+│   │   ├── ./data/processed/In_Est
+│   │   │   ├── ./data/processed/In_Est/EXTREME
+│   │   │   ├── ./data/processed/In_Est/NORMAL
+│   │   │   ├── ./data/processed/In_Est/SEVERE
+│   │   │   ├── ./data/processed/In_Est/SUPER
+│   │   │   └── ./data/processed/In_Est/VERY
+│   │   ├── ./data/processed/Obj_Det
+│   │   │   ├── ./data/processed/Obj_Det/images
+│   │   │   ├── ./data/processed/Obj_Det/labels.csv
+│   │   │   ├── ./data/processed/Obj_Det/test
+│   │   │   ├── ./data/processed/Obj_Det/test_labels.csv
+│   │   │   ├── ./data/processed/Obj_Det/test.record
+│   │   │   ├── ./data/processed/Obj_Det/train
+│   │   │   │   ├── ./data/processed/Obj_Det/train/f1
+│   │   │   ├── ./data/processed/Obj_Det/train_labels.csv
+│   │   │   └── ./data/processed/Obj_Det/train.record
+│   │   └── ./data/processed/threshZero_data
+│   │       ├── ./data/processed/threshZero_data/EXTREME
+│   │       │   ├── ./data/processed/threshZero_data/EXTREME/f2
+│   │       │   └── ./data/processed/threshZero_data/EXTREME/f3
+│   │       ├── ./data/processed/threshZero_data/NORMAL
+│   │       │   ├── ./data/processed/threshZero_data/NORMAL/f2
+│   │       │   └── ./data/processed/threshZero_data/NORMAL/f3
+│   │       ├── ./data/processed/threshZero_data/SEVERE
+│   │       │   ├── ./data/processed/threshZero_data/SEVERE/Annotations
+│   │       │   ├── ./data/processed/threshZero_data/SEVERE/f2
+│   │       │   └── ./data/processed/threshZero_data/SEVERE/f3
+│   │       ├── ./data/processed/threshZero_data/SUPER
+│   │       │   ├── ./data/processed/threshZero_data/SUPER/Annotations
+│   │       │   ├── ./data/processed/threshZero_data/SUPER/f2
+│   │       │   └── ./data/processed/threshZero_data/SUPER/f3
+│   │       └── ./data/processed/threshZero_data/VERY
+│   │           ├── ./data/processed/threshZero_data/VERY/Annotations
+│   │           ├── ./data/processed/threshZero_data/VERY/f2
+│   │           └── ./data/processed/threshZero_data/VERY/f3
+│   └── ./data/raw
+│       ├── ./data/raw/APRIL_2019 Fani
+│       ├── ./data/raw/final
+│       ├── ./data/raw/MAY_2020 Amphan
+│       ├── ./data/raw/MAY_2021 Yaas Tauktae
+│       ├── ./data/raw/NOVEMBER_2020 Nivar
+│       ├── ./data/raw/OCTOBER_2019 Kyarr
+│       └── ./data/raw/VERY
+```
+
+# System Architecture
+
+VORTEX is built using a Multi-Model architecture. The first model takes as input an infrared image of a Semi-Globe containing India and detects all the cyclones from a specific region. Then the detected tropical cyclone is sent to the second model and outputs a prediction of its intensity, specifically, classifies the cyclone as one of the 5 severities. The architecture is designed to use the trained models as a web service where real-time data can be fed to the model and the output can be used by weather forecasters and disaster management agencies to make informed decisions.
 
-VORTEX is built using a convolutional neural network (CNN) architecture. The model takes as input an infrared image of a tropical cyclone and outputs a prediction of its intensity. The architecture is designed to extract features from the input images and make use of them in the final prediction.
+![Architecture](./assets/docs/SIH%20Docs/System%20Architecture%20Diagram.png)
 
 # Training and Evaluation
 
 The model is trained on a large dataset of historical tropical cyclone images and corresponding intensity estimates. The training process involves minimizing the mean squared error between the predicted intensity and the ground truth intensity. The model is evaluated on a hold-out test set to determine its accuracy and precision.
 
+```bash
+└── ./vortex
+    ├── ./vortex/artifacts
+    │   ├── ./vortex/artifacts/border_box
+    │   │   ├── ./vortex/artifacts/border_box/detector.h5
+    │   │   ├── ./vortex/artifacts/border_box/plot.png
+    │   │   ├── ./vortex/artifacts/border_box/test_images.txt
+    │   │   └── ./vortex/artifacts/border_box/tfjs
+    │   │       └── ./vortex/artifacts/border_box/tfjs/detector
+    │   │           └── ./vortex/artifacts/border_box/tfjs/detector/model.json
+    │   ├── ./vortex/artifacts/in_estimator
+    │   │   ├── ./vortex/artifacts/in_estimator/in_estimator_2.h5
+    │   │   └── ./vortex/artifacts/in_estimator/in_estimator.h5
+    │   ├── ./vortex/artifacts/inference_graph
+    │   │   ├── ./vortex/artifacts/inference_graph/checkpoint
+    │   │   ├── ./vortex/artifacts/inference_graph/pipeline.config
+    │   │   └── ./vortex/artifacts/inference_graph/saved_model
+    ├── ./vortex/hybridPredict.ipynb
+    ├── ./vortex/hybridPred.py
+    ├── ./vortex/notebooks
+    │   ├── ./vortex/notebooks/border_box
+    │   │   ├── ./vortex/notebooks/border_box/config.py
+    │   │   ├── ./vortex/notebooks/border_box/__init__.py
+    │   │   ├── ./vortex/notebooks/border_box/predict.py
+    │   ├── ./vortex/notebooks/Border Box Regression.ipynb
+    │   ├── ./vortex/notebooks/cropping.ipynb
+    │   ├── ./vortex/notebooks/Cyclone_Detection.ipynb
+    │   ├── ./vortex/notebooks/in_estimator
+    │   │   ├── ./vortex/notebooks/in_estimator/__init__.py
+    │   │   └── ./vortex/notebooks/in_estimator/predict.py
+    │   ├── ./vortex/notebooks/inferenceutils.py
+    │   ├── ./vortex/notebooks/__init__.py
+    │   ├── ./vortex/notebooks/Intensity Classifier .ipynb
+    ├── ./vortex/training
+    │   ├── ./vortex/training/checkpoint
+    │   ├── ./vortex/training/eval
+    └── ./vortex/utils
+        ├── ./vortex/utils/generate_tfrecord.py
+        ├── ./vortex/utils/inferenceutils.py
+        └── ./vortex/utils/test_train_split.py
+```
+
 # Deployment
 
 The trained VORTEX model can be deployed as a web service or as a standalone application. The service/application takes as input an infrared image of a tropical cyclone and returns a prediction of its intensity. This information can be used by weather forecasters and disaster management agencies to make informed decisions.