Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("derhuli/vit-base-beans")
model = AutoModelForImageClassification.from_pretrained("derhuli/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: 0.0410
- Accuracy: 0.9925
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0751 | 1.54 | 100 | 0.0768 | 0.9850 |
| 0.0121 | 3.08 | 200 | 0.0410 | 0.9925 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.10.0
- Datasets 2.7.1
- Tokenizers 0.13.2
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Dataset used to train derhuli/vit-base-beans
Evaluation results
- Accuracy on beansself-reported0.992
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="derhuli/vit-base-beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")