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Vit Finetuned

This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset.

KindOtherBase modelgoogle/vit-base-patch16-224VersionRevision be7193b

Model source

Kind
Other
File size
327 MB
Base model
google/vit-base-patch16-224
Version
vRevision be7193b
Updated
Sep 28, 2026
Downloads
11
Source
Hugging Face

Source description

vit-finetuned

This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset.

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Framework versions

  • Transformers 5.9.0
  • Pytorch 2.11.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

Sources

1 source
Hugging FacePrimaryVersion be7193bcb42ec10a54882fb6848a7b53b379f4bf
huggingface.co/sasha/vit-finetuned/tree/be7193bcb42ec10a54882fb6848a7b53b379f4bf

Verified Sep 28

Model artifacts

1 artifact

model.safetensors

safetensors · 327 MB · SHA-256 db9a88d1b967…1f85 · Hugging Face

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Source excerpts

2 excerpts

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer model-index: - name: vit-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-finetuned This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 5.9.0 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 11 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo sasha/vit-finetuned