Update README.md

README.md

@@ -1,3 +1,48 @@
----
-license: apache-2.0
----
+---
+pipeline_tag: image-classification
+license: apache-2.0
+---
+
+# Model Card: Fine-Tuned InceptionV3 for Human Bodypart Image Classification
+
+This CNN model was developed to perform human bodypart classification. 
+
+## Model Details
+
+### Model Description
+
+- **Funded by:** National Institute of Justice
+- **Model type:** CNNs for Image Classification
+- **Base Model:** InceptionV3 pretrained on ImageNet
+
+## Dataset
+
+- Dataset Name: Human Decomposition Image 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: 'arm', 'hand', 'foot', 'legs','fullbody','head','backside', 'torso', 'stake', 'plastic'. 'stake' and 'plastic' classes were
+included for filtering out images where bodyparts are covered with plastic or images showing stake with unanonymized donor IDs,
+which is often the case in forensic anthropology.
+
+## Usage
+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)
+```