---
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: 0.0748
- Train Accuracy: 0.9743
- Validation Loss: 0.6597
- Validation Accuracy: 0.8575
- Epoch: 49

## 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    |
| 0.5083     | 0.8459         | 0.7343          | 0.7990              | 11    |
| 0.4695     | 0.8559         | 0.6768          | 0.8075              | 12    |
| 0.4432     | 0.8615         | 0.6830          | 0.8095              | 13    |
| 0.4125     | 0.8704         | 0.6891          | 0.7980              | 14    |
| 0.3995     | 0.875          | 0.6482          | 0.8155              | 15    |
| 0.3723     | 0.8781         | 0.6653          | 0.8095              | 16    |
| 0.3505     | 0.8859         | 0.6268          | 0.8195              | 17    |
| 0.3390     | 0.8906         | 0.6243          | 0.8205              | 18    |
| 0.3132     | 0.8967         | 0.6338          | 0.8255              | 19    |
| 0.2879     | 0.9071         | 0.5879          | 0.8380              | 20    |
| 0.2845     | 0.9066         | 0.6004          | 0.8320              | 21    |
| 0.2578     | 0.9141         | 0.6228          | 0.8320              | 22    |
| 0.2521     | 0.9178         | 0.6208          | 0.8295              | 23    |
| 0.2375     | 0.9258         | 0.6051          | 0.8410              | 24    |
| 0.2226     | 0.9243         | 0.6138          | 0.8395              | 25    |
| 0.2139     | 0.9298         | 0.5651          | 0.8455              | 26    |
| 0.2094     | 0.9302         | 0.5881          | 0.8470              | 27    |
| 0.1925     | 0.9385         | 0.6298          | 0.8390              | 28    |
| 0.1806     | 0.9399         | 0.5982          | 0.8450              | 29    |
| 0.1758     | 0.9401         | 0.6139          | 0.8435              | 30    |
| 0.1630     | 0.9449         | 0.6105          | 0.8430              | 31    |
| 0.1566     | 0.9449         | 0.5953          | 0.8490              | 32    |
| 0.1423     | 0.9531         | 0.6246          | 0.8440              | 33    |
| 0.1378     | 0.9545         | 0.6249          | 0.8500              | 34    |
| 0.1379     | 0.9553         | 0.6625          | 0.8415              | 35    |
| 0.1305     | 0.9551         | 0.6035          | 0.8575              | 36    |
| 0.1253     | 0.9581         | 0.6503          | 0.8490              | 37    |
| 0.1149     | 0.9607         | 0.5882          | 0.8585              | 38    |
| 0.1026     | 0.9672         | 0.6130          | 0.8530              | 39    |
| 0.1019     | 0.9660         | 0.6373          | 0.8525              | 40    |
| 0.1038     | 0.9645         | 0.6197          | 0.8570              | 41    |
| 0.0938     | 0.9685         | 0.6239          | 0.8545              | 42    |
| 0.0910     | 0.9688         | 0.6439          | 0.8590              | 43    |
| 0.0869     | 0.9711         | 0.5812          | 0.8640              | 44    |
| 0.0818     | 0.9726         | 0.6692          | 0.8565              | 45    |
| 0.0695     | 0.9799         | 0.6652          | 0.8585              | 46    |
| 0.0756     | 0.9765         | 0.6584          | 0.8570              | 47    |
| 0.0669     | 0.9797         | 0.6542          | 0.8610              | 48    |
| 0.0748     | 0.9743         | 0.6597          | 0.8575              | 49    |


### Framework versions

- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1