Instructions to use christyli/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christyli/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="christyli/vit-base-beans") 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("christyli/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("christyli/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
vit-base-beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3930
- Accuracy: 0.9774
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0349 | 1.0 | 17 | 0.8167 | 0.9323 |
| 0.7502 | 2.0 | 34 | 0.6188 | 0.9699 |
| 0.5508 | 3.0 | 51 | 0.4856 | 0.9774 |
| 0.4956 | 4.0 | 68 | 0.4109 | 0.9774 |
| 0.4261 | 5.0 | 85 | 0.3930 | 0.9774 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Tokenizers 0.12.1
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