Instructions to use jonathanfernandes/vit-base-patch16-224-finetuned-flower with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonathanfernandes/vit-base-patch16-224-finetuned-flower with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jonathanfernandes/vit-base-patch16-224-finetuned-flower") 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("jonathanfernandes/vit-base-patch16-224-finetuned-flower") model = AutoModelForImageClassification.from_pretrained("jonathanfernandes/vit-base-patch16-224-finetuned-flower") - Notebooks
- Google Colab
- Kaggle
vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
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