# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("fassabilf/results")
model = AutoModelForImageClassification.from_pretrained("fassabilf/results")Quick Links
results
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.3919
- eval_accuracy: 0.4688
- eval_runtime: 22.7841
- eval_samples_per_second: 7.022
- eval_steps_per_second: 0.219
- epoch: 12.65
- step: 253
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 20
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for fassabilf/results
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="fassabilf/results") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")