Instructions to use durovali/vit-motorcycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use durovali/vit-motorcycle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="durovali/vit-motorcycle") 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("durovali/vit-motorcycle") model = AutoModelForImageClassification.from_pretrained("durovali/vit-motorcycle") - Notebooks
- Google Colab
- Kaggle
vit-motorcycle
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: 1.7219
- Accuracy: 0.2727
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.8584 | 1.0 | 3 | 1.7152 | 0.1818 |
| 1.7491 | 2.0 | 6 | 1.6670 | 0.2727 |
| 1.4504 | 3.0 | 9 | 1.6373 | 0.2727 |
| 1.5355 | 4.0 | 12 | 1.6697 | 0.2727 |
| 1.4388 | 5.0 | 15 | 1.7158 | 0.2727 |
| 1.1518 | 6.0 | 18 | 1.7386 | 0.2727 |
| 1.5075 | 7.0 | 21 | 1.7460 | 0.2727 |
| 1.5540 | 8.0 | 24 | 1.7286 | 0.2727 |
| 1.7147 | 9.0 | 27 | 1.7242 | 0.2727 |
| 1.7869 | 10.0 | 30 | 1.7219 | 0.2727 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for durovali/vit-motorcycle
Base model
google/vit-base-patch16-224-in21k