transformers pytorch vit image-classification generated_from_keras_callback base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: dwiedarioo/vit-base-patch16-224-in21k-brainmri
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# winwithaman/vit-base-patch16-224-in21k-finetuned-brain-ich

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an brain hemorrhage dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2848
- Train Accuracy: 0.9969
- Train Top-3-accuracy: 0.9992
- Validation Loss: 0.3786
- Validation Accuracy: 0.9590
- Validation Top-3-accuracy: 0.9892
- Epoch: 7

## 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:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1230, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16

### Training results

| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 2.2199     | 0.4215         | 0.6564               | 1.8634          | 0.5702              | 0.8099                    | 0     |
| 1.5448     | 0.6976         | 0.8797               | 1.3110          | 0.7603              | 0.9028                    | 1     |
| 1.0494     | 0.8694         | 0.9519               | 0.9507          | 0.8855              | 0.9590                    | 2     |
| 0.7408     | 0.9381         | 0.9824               | 0.7499          | 0.9114              | 0.9806                    | 3     |
| 0.5428     | 0.9756         | 0.9939               | 0.5831          | 0.9460              | 0.9849                    | 4     |
| 0.4169     | 0.9901         | 0.9977               | 0.4895          | 0.9525              | 0.9914                    | 5     |
| 0.3371     | 0.9947         | 0.9977               | 0.4194          | 0.9611              | 0.9892                    | 6     |
| 0.2848     | 0.9969         | 0.9992               | 0.3786          | 0.9590              | 0.9892                    | 7     |


### Framework versions

- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
