# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Pamreth/vit-ena24")
model = AutoModelForImageClassification.from_pretrained("Pamreth/vit-ena24")Quick Links
vit-ena24
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:
- eval_loss: 3.0899
- eval_model_preparation_time: 0.0031
- eval_accuracy: 0.0435
- eval_runtime: 925.7714
- eval_samples_per_second: 1.415
- eval_steps_per_second: 0.177
- step: 0
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: 8
- eval_batch_size: 8
- 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: 2
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Pamreth/vit-ena24
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="Pamreth/vit-ena24") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")