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---
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: KDRSSC_TinyViT2MobileViT-xx-small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KDRSSC_TinyViT2MobileViT-xx-small
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:
- Loss: 0.8217
- Accuracy: 0.8398
- Precision: 0.8409
- Recall: 0.8398
- F1: 0.8365
## 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:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.1113 | 1.0 | 148 | 1.7471 | 0.588 | 0.6313 | 0.588 | 0.5698 |
| 1.6003 | 2.0 | 296 | 1.3462 | 0.704 | 0.7133 | 0.704 | 0.6844 |
| 1.2989 | 3.0 | 444 | 1.1278 | 0.759 | 0.7716 | 0.759 | 0.7509 |
| 1.1115 | 4.0 | 592 | 0.9891 | 0.802 | 0.8022 | 0.802 | 0.7952 |
| 0.9978 | 5.0 | 740 | 0.9123 | 0.827 | 0.8413 | 0.827 | 0.8255 |
| 0.9274 | 6.0 | 888 | 0.8512 | 0.843 | 0.8445 | 0.843 | 0.8387 |
| 0.8748 | 7.0 | 1036 | 0.8210 | 0.842 | 0.8412 | 0.842 | 0.8373 |
| 0.8411 | 8.0 | 1184 | 0.7952 | 0.842 | 0.8398 | 0.842 | 0.8365 |
| 0.818 | 9.0 | 1332 | 0.7814 | 0.852 | 0.8574 | 0.852 | 0.8489 |
| 0.8081 | 10.0 | 1480 | 0.7796 | 0.853 | 0.8591 | 0.853 | 0.8487 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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