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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Alph0nse/vit-base-patch16-224-in21k_v2_breed_cls_v2
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. -->
# Alph0nse/vit-base-patch16-224-in21k_v2_breed_cls_v2
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: 0.8190
- Train Accuracy: 0.9248
- Train Top-3-accuracy: 0.9777
- Validation Loss: 0.9820
- Validation Accuracy: 0.9308
- Validation Top-3-accuracy: 0.9799
- Epoch: 4
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 560, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 2.4175 | 0.5263 | 0.7190 | 1.9955 | 0.7702 | 0.9039 | 0 |
| 1.5487 | 0.8270 | 0.9344 | 1.4502 | 0.8624 | 0.9519 | 1 |
| 1.1223 | 0.8829 | 0.9609 | 1.1583 | 0.8982 | 0.9674 | 2 |
| 0.9127 | 0.9094 | 0.9718 | 1.0461 | 0.9181 | 0.9753 | 3 |
| 0.8190 | 0.9248 | 0.9777 | 0.9820 | 0.9308 | 0.9799 | 4 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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