Instructions to use Docty/mangoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/mangoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/mangoes") 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("Docty/mangoes") model = AutoModelForImageClassification.from_pretrained("Docty/mangoes") - Notebooks
- Google Colab
- Kaggle
Image Classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Docty/Mangovariety dataset.
You can find some example images in the following.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for Docty/mangoes
Base model
google/vit-base-patch16-224-in21k






