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---
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_keras_callback
model-index:
- name: hafizurUMaine/cifar10_m
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. -->
# hafizurUMaine/cifar10_m
This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5525
- Train Accuracy: 0.8321
- Validation Loss: 0.7367
- Validation Accuracy: 0.7955
- Epoch: 10
## 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': 400000, '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 | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.5748 | 0.1482 | 3.1655 | 0.4160 | 0 |
| 2.4468 | 0.5135 | 1.7772 | 0.6195 | 1 |
| 1.5927 | 0.6389 | 1.3152 | 0.6770 | 2 |
| 1.2333 | 0.7001 | 1.1226 | 0.7265 | 3 |
| 1.0094 | 0.7334 | 0.9668 | 0.7490 | 4 |
| 0.8748 | 0.7591 | 0.9140 | 0.7510 | 5 |
| 0.7714 | 0.7846 | 0.7881 | 0.7845 | 6 |
| 0.6977 | 0.7999 | 0.8075 | 0.7745 | 7 |
| 0.6524 | 0.8096 | 0.8417 | 0.7675 | 8 |
| 0.5904 | 0.8254 | 0.7763 | 0.7850 | 9 |
| 0.5525 | 0.8321 | 0.7367 | 0.7955 | 10 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|