---
license: apache-2.0
base_model: ansilmbabl/vit-base-patch16-224-in21k-cards-june-07-cropping-filtered-preprocess-change-test
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-cards-june-08-cropping-filtered-preprocess-change-test-2
  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-base-patch16-224-in21k-cards-june-08-cropping-filtered-preprocess-change-test-2

This model is a fine-tuned version of [ansilmbabl/vit-base-patch16-224-in21k-cards-june-07-cropping-filtered-preprocess-change-test](https://huggingface.co/ansilmbabl/vit-base-patch16-224-in21k-cards-june-07-cropping-filtered-preprocess-change-test) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5958
- Accuracy: 0.5147

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Accuracy | Validation Loss |
|:-------------:|:------:|:-----:|:--------:|:---------------:|
| 1.0182        | 0.9998 | 1298  | 0.4287   | 1.5280          |
| 0.9583        | 1.9996 | 2596  | 0.4475   | 1.4878          |
| 0.8452        | 2.9998 | 3894  | 1.4847   | 0.4716          |
| 0.6887        | 3.9996 | 5192  | 1.5848   | 0.4736          |
| 0.5269        | 4.9994 | 6490  | 1.6689   | 0.493           |
| 0.4018        | 6.0    | 7789  | 1.8483   | 0.4986          |
| 0.2909        | 6.9998 | 9087  | 2.0319   | 0.5079          |
| 0.1823        | 7.9996 | 10385 | 2.2540   | 0.5127          |
| 0.1056        | 8.9994 | 11683 | 2.4652   | 0.511           |
| 0.0767        | 9.9985 | 12980 | 2.5958   | 0.5147          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1