# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("afford6522/vit-beans-classifier")
model = AutoModelForImageClassification.from_pretrained("afford6522/vit-beans-classifier")Quick Links
vit-beans-classifier
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:
- Loss: 0.0349
- Accuracy: 0.9850
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 65 | 0.1429 | 0.9624 |
| No log | 2.0 | 130 | 0.0579 | 0.9850 |
| No log | 3.0 | 195 | 0.0418 | 0.9850 |
| No log | 4.0 | 260 | 0.0361 | 0.9850 |
| No log | 5.0 | 325 | 0.0349 | 0.9850 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0
- Datasets 4.3.0
- Tokenizers 0.22.1
- Downloads last month
- 1
Model tree for afford6522/vit-beans-classifier
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="afford6522/vit-beans-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")