# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/smoker_image_classification")
model = AutoModelForImageClassification.from_pretrained("dima806/smoker_image_classification")Quick Links
Returns whether the person is a smoker based on image with about 97% accuracy.
See https://www.kaggle.com/code/dima806/smoker-image-detection-vit for more details.
Classification report:
precision recall f1-score support
notsmoking 0.9907 0.9464 0.9680 112
smoking 0.9487 0.9911 0.9694 112
accuracy 0.9688 224
macro avg 0.9697 0.9688 0.9687 224
weighted avg 0.9697 0.9688 0.9687 224
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Model tree for dima806/smoker_image_classification
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/smoker_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")