# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/full_flat_tyre_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/full_flat_tyre_image_detection")Quick Links
Check whether the tyre is flat given an image.
See https://www.kaggle.com/code/dima806/full-flat-tyre-image-detection-vit for more details.
Classification report:
precision recall f1-score support
flat 1.0000 1.0000 1.0000 60
no-tire 1.0000 1.0000 1.0000 60
full 1.0000 1.0000 1.0000 60
accuracy 1.0000 180
macro avg 1.0000 1.0000 1.0000 180
weighted avg 1.0000 1.0000 1.0000 180
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Model tree for dima806/full_flat_tyre_image_detection
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/full_flat_tyre_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")