# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("hanyp/vit-base-beans")
model = AutoModelForImageClassification.from_pretrained("hanyp/vit-base-beans")Quick Links
vit-base-beans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.1065
- 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: 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 |
|---|---|---|---|---|
| 0.4659 | 1.0 | 65 | 0.3748 | 0.9624 |
| 0.2039 | 2.0 | 130 | 0.1851 | 0.9774 |
| 0.1747 | 3.0 | 195 | 0.1309 | 0.9774 |
| 0.1496 | 4.0 | 260 | 0.1065 | 0.9850 |
| 0.1125 | 5.0 | 325 | 0.1163 | 0.9774 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for hanyp/vit-base-beans
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hanyp/vit-base-beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")