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---
library_name: transformers
license: other
base_model: apple/mobilevit-x-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilevit-x-small
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.995850622406639
---
<!-- 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. -->
# mobilevit-x-small
This model is a fine-tuned version of [apple/mobilevit-x-small](https://huggingface.co/apple/mobilevit-x-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0196
- Accuracy: 0.9959
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6911 | 1.0 | 34 | 0.6932 | 0.5083 |
| 0.6584 | 2.0 | 68 | 0.6287 | 0.7510 |
| 0.5388 | 3.0 | 102 | 0.4852 | 0.8734 |
| 0.3891 | 4.0 | 136 | 0.3065 | 0.9357 |
| 0.2915 | 5.0 | 170 | 0.2005 | 0.9647 |
| 0.2319 | 6.0 | 204 | 0.1498 | 0.9689 |
| 0.2038 | 7.0 | 238 | 0.1228 | 0.9710 |
| 0.1641 | 8.0 | 272 | 0.0892 | 0.9855 |
| 0.1525 | 9.0 | 306 | 0.0778 | 0.9834 |
| 0.1584 | 10.0 | 340 | 0.0565 | 0.9896 |
| 0.1194 | 11.0 | 374 | 0.0491 | 0.9917 |
| 0.1222 | 12.0 | 408 | 0.0436 | 0.9896 |
| 0.1229 | 13.0 | 442 | 0.0360 | 0.9979 |
| 0.1334 | 14.0 | 476 | 0.0326 | 0.9959 |
| 0.122 | 15.0 | 510 | 0.0425 | 0.9896 |
| 0.096 | 16.0 | 544 | 0.0315 | 0.9959 |
| 0.0989 | 17.0 | 578 | 0.0303 | 0.9938 |
| 0.1085 | 18.0 | 612 | 0.0262 | 0.9959 |
| 0.0957 | 19.0 | 646 | 0.0232 | 0.9959 |
| 0.1129 | 20.0 | 680 | 0.0266 | 0.9959 |
| 0.0843 | 21.0 | 714 | 0.0234 | 0.9959 |
| 0.0868 | 22.0 | 748 | 0.0217 | 0.9959 |
| 0.0867 | 23.0 | 782 | 0.0233 | 0.9959 |
| 0.0947 | 24.0 | 816 | 0.0204 | 0.9959 |
| 0.0786 | 25.0 | 850 | 0.0199 | 0.9959 |
| 0.1009 | 26.0 | 884 | 0.0212 | 0.9959 |
| 0.0785 | 27.0 | 918 | 0.0204 | 0.9959 |
| 0.0811 | 28.0 | 952 | 0.0180 | 0.9959 |
| 0.0883 | 29.0 | 986 | 0.0193 | 0.9959 |
| 0.0988 | 30.0 | 1020 | 0.0196 | 0.9959 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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