Instructions to use andrei-saceleanu/vit-base-freematch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andrei-saceleanu/vit-base-freematch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="andrei-saceleanu/vit-base-freematch")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("andrei-saceleanu/vit-base-freematch") model = AutoModel.from_pretrained("andrei-saceleanu/vit-base-freematch") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vit-base-freematch
results: []
vit-base-freematch
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Tokenizers 0.13.3