Instructions to use binjn/vit-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binjn/vit-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="binjn/vit-finetuned") 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("binjn/vit-finetuned") model = AutoModelForImageClassification.from_pretrained("binjn/vit-finetuned") - Notebooks
- Google Colab
- Kaggle
vit-finetuned
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2256
- Accuracy: 0.43
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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 | 211 | 2.9776 | 0.252 |
| No log | 2.0 | 422 | 2.6746 | 0.326 |
| 2.9731 | 3.0 | 633 | 2.4969 | 0.362 |
| 2.9731 | 4.0 | 844 | 2.2917 | 0.434 |
| 2.1058 | 5.0 | 1055 | 2.2256 | 0.43 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
- 2
Model tree for binjn/vit-finetuned
Base model
google/vit-base-patch16-224