transformers tf tensorboard 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: suryanshgoel/vit-base-patch16-224-in21k-euroSat
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# suryanshgoel/vit-base-patch16-224-in21k-euroSat

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 unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5270
- Train Accuracy: 0.9965
- Train Top-3-accuracy: 0.9990
- Validation Loss: 1.2029
- Validation Accuracy: 0.7352
- Validation Top-3-accuracy: 0.9352
- Epoch: 9

## 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': 6300, '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 |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 3.1420     | 0.3167         | 0.5274               | 2.9076          | 0.4394              | 0.6958                    | 0     |
| 2.6104     | 0.5642         | 0.8297               | 2.4594          | 0.5577              | 0.8282                    | 1     |
| 2.1588     | 0.6783         | 0.9168               | 2.1385          | 0.6225              | 0.8789                    | 2     |
| 1.7981     | 0.7689         | 0.9612               | 1.8776          | 0.6732              | 0.9042                    | 3     |
| 1.4937     | 0.8381         | 0.9816               | 1.6875          | 0.6958              | 0.9014                    | 4     |
| 1.2306     | 0.8979         | 0.9910               | 1.5326          | 0.7211              | 0.9211                    | 5     |
| 1.0046     | 0.9377         | 0.9940               | 1.4043          | 0.7183              | 0.9296                    | 6     |
| 0.8088     | 0.9731         | 0.9975               | 1.3022          | 0.7408              | 0.9268                    | 7     |
| 0.6470     | 0.9905         | 0.9980               | 1.2706          | 0.7352              | 0.9296                    | 8     |
| 0.5270     | 0.9965         | 0.9990               | 1.2029          | 0.7352              | 0.9352                    | 9     |


### Framework versions

- Transformers 4.32.1
- TensorFlow 2.15.0
- Datasets 2.12.0
- Tokenizers 0.13.3
