Instructions to use therealcyberlord/bigcatvit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use therealcyberlord/bigcatvit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="therealcyberlord/bigcatvit") 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("therealcyberlord/bigcatvit") model = AutoModelForImageClassification.from_pretrained("therealcyberlord/bigcatvit") - Notebooks
- Google Colab
- Kaggle
Fine-tuning a Vision Transformer on the Big Cats Dataset In this project, we fine-tuned a vision transformer on the Big Cats dataset to perform image classification. The Big Cats dataset consists of 2339 images of 10 different types of big cats, including lions, tigers, jaguars, and more.
Our goal was to train a model that could accurately classify these images with high accuracy. After fine-tuning a pre-trained Vision Transformer, we were able to achieve an accuracy of 98%.
Kaggle dataset: https://www.kaggle.com/datasets/gpiosenka/cats-in-the-wild-image-classification

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