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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: 0.0748
- 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
|