Instructions to use fehrnic1/alpaca-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fehrnic1/alpaca-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fehrnic1/alpaca-vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("fehrnic1/alpaca-vit") model = AutoModelForImageClassification.from_pretrained("fehrnic1/alpaca-vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
alpaca-vit-hub
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1684
- Accuracy: 1.0
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 13 | 0.4747 | 0.6923 |
| No log | 2.0 | 26 | 0.3088 | 0.9615 |
| No log | 3.0 | 39 | 0.2297 | 1.0 |
| No log | 4.0 | 52 | 0.1907 | 1.0 |
| No log | 5.0 | 65 | 0.1786 | 1.0 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.11.0+cpu
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for fehrnic1/alpaca-vit
Base model
google/vit-base-patch16-224Space using fehrnic1/alpaca-vit 1
Evaluation results
- Accuracy on imagefolderself-reported1.000