Instructions to use dima806/10_ship_types_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/10_ship_types_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/10_ship_types_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/10_ship_types_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/10_ship_types_image_detection") - Notebooks
- Google Colab
- Kaggle
Returns ship type given an image with about 99.6% accuracy.
See https://www.kaggle.com/code/dima806/ship-type-detection-vit for more details.
Classification report:
precision recall f1-score support
Bulkers 0.9927 1.0000 0.9963 409
Recreational 0.9902 0.9927 0.9915 409
Sailboat 0.9975 0.9853 0.9914 409
DDG 0.9976 1.0000 0.9988 409
Container Ship 1.0000 0.9951 0.9975 409
Tug 0.9951 0.9927 0.9939 410
Aircraft Carrier 1.0000 0.9976 0.9988 409
Cruise 1.0000 1.0000 1.0000 409
Submarine 0.9927 1.0000 0.9964 410
Car Carrier 0.9951 0.9976 0.9963 409
accuracy 0.9961 4092
macro avg 0.9961 0.9961 0.9961 4092
weighted avg 0.9961 0.9961 0.9961 4092
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Model tree for dima806/10_ship_types_image_detection
Base model
google/vit-base-patch16-224-in21k