Instructions to use Braywayc/autotrain-n-64-image-classifier-99356147309 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Braywayc/autotrain-n-64-image-classifier-99356147309 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Braywayc/autotrain-n-64-image-classifier-99356147309") 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("Braywayc/autotrain-n-64-image-classifier-99356147309") model = AutoModelForImageClassification.from_pretrained("Braywayc/autotrain-n-64-image-classifier-99356147309", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Fake Nintendo-64 cartridge detector
tags: - autotrain - vision - image-classification datasets: - Braywayc/autotrain-data-n-64-image-classifier widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.3921587188762522
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 99356147309
- CO2 Emissions (in grams): 0.3922
Validation Metrics
- Loss: 0.429
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
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