Instructions to use dima806/tesla_car_model_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/tesla_car_model_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/tesla_car_model_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/tesla_car_model_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/tesla_car_model_image_detection") - Notebooks
- Google Colab
- Kaggle
Returns Tesla car model given an image with about 85% accuracy.
See https://www.kaggle.com/code/dima806/tesla-car-model-image-detection-vit for more details.
Classification report:
precision recall f1-score support
Model_Y 0.8679 0.8364 0.8519 55
Model_E 0.8462 0.8800 0.8627 100
Model_S 0.8293 0.8095 0.8193 42
Model_X 0.8519 0.8364 0.8440 55
accuracy 0.8492 252
macro avg 0.8488 0.8406 0.8445 252
weighted avg 0.8493 0.8492 0.8490 252
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Model tree for dima806/tesla_car_model_image_detection
Base model
google/vit-base-patch16-224-in21k