# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/vessel_ship_types_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/vessel_ship_types_image_detection")Quick Links
Returns vessel/ship type based on image with about 99% accuracy.
See https://www.kaggle.com/code/dima806/vessel-ship-type-detection for more details.
Classification report:
precision recall f1-score support
Cargo 0.9927 0.9623 0.9772 424
Carrier 0.9976 1.0000 0.9988 424
Cruise 1.0000 1.0000 1.0000 424
Military 0.9976 0.9976 0.9976 424
Tankers 0.9679 0.9953 0.9814 424
accuracy 0.9910 2120
macro avg 0.9912 0.9910 0.9910 2120
weighted avg 0.9912 0.9910 0.9910 2120
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Model tree for dima806/vessel_ship_types_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/vessel_ship_types_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")