Image Classification
Transformers
Safetensors
vit
beans
no-augmentation
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("remonemo/beans_no_aug")
model = AutoModelForImageClassification.from_pretrained("remonemo/beans_no_aug")Quick Links
beans_no_aug
This model is a fine-tuned version of google/vit-base-patch16-224 on the nateraw/beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.1538
- Accuracy: 0.9766
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: 16
- seed: 42
- optimizer: Use 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: 4
Training results
Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for remonemo/beans_no_aug
Base model
google/vit-base-patch16-224Dataset used to train remonemo/beans_no_aug
Evaluation results
- Accuracy on nateraw/beansvalidation set self-reported0.977
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="remonemo/beans_no_aug") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")