Update README.md

README.md

@@ -22,7 +22,33 @@ These CNN models were developed for the classification of human decomposition im
   - [Stage of Decay Estimation Exploiting Exogenous and Endogenous Image Attributes to Minimize Manual Labeling Efforts and Maximize Classification Performance](https://ieeexplore.ieee.org/abstract/document/10222106)
   - [Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach](https://arxiv.org/abs/2408.10414)
 
+
+## Dataset
+
+- Dataset Name: Human Decomposition Dataset
+- Source: The dataset used in this study was obtained from the Forensic Anthropology Center (FAC) at the University of Tennessee, Knoxville, but due to privacy considerations, it is not available for public access. Please reach out to obtain access.
+- Classes: 1-6 (least to most decay) based on [Gelderman et al's](https://link.springer.com/article/10.1007/s00414-017-1700-9) scoring method.
+
 ## Usage
-The stage of decay classification is bodypart specific, that is, for the head, torso, or limbs.
+The stage of decay classification is bodypart specific (i.e., head, torso, or limbs), so make sure to pick the correct bodypart model.
+
+```python
+from tensorflow.keras.models import load_model
+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+
+# Load the entire model
+model = load_model('path_to_your_model')  # e.g. head/inceptionV3 to perform stage of decay classfication of head images
+
+# Load and preprocess an image
+img = load_img('path_to_image.jpg', target_size=(299, 299))  # adjust size as per model input
+img = img_to_array(img)  # convert to numpy array
+img = np.expand_dims(img, axis=0)  # add batch dimension
+img = img / 255.0  # normalize pixel values if needed
 
+# Make predictions
+predictions = model.predict(img)
 
+# Use argmax to get the class label 
+predicted_class = np.argmax(predictions, axis=1)
+```