metadata
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.
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)