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---
library_name: transformers
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
datasets:
- webdataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: frost-mobile-apple__mobilevit-xx-small-v2024-10-22
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: webdataset
type: webdataset
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9497777777777778
- name: F1
type: f1
value: 0.8754134509371555
- name: Precision
type: precision
value: 0.8744493392070485
- name: Recall
type: recall
value: 0.8763796909492274
---
<!-- 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. -->
# frost-mobile-apple__mobilevit-xx-small-v2024-10-22
This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co./apple/mobilevit-xx-small) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1343
- Accuracy: 0.9498
- F1: 0.8754
- Precision: 0.8744
- Recall: 0.8764
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1927 | 1.7544 | 100 | 0.1470 | 0.9422 | 0.8565 | 0.8565 | 0.8565 |
| 0.1601 | 3.5088 | 200 | 0.1499 | 0.9444 | 0.8616 | 0.8644 | 0.8587 |
| 0.1544 | 5.2632 | 300 | 0.1536 | 0.9391 | 0.8493 | 0.8465 | 0.8521 |
| 0.207 | 7.0175 | 400 | 0.1374 | 0.9436 | 0.8575 | 0.8721 | 0.8433 |
| 0.1709 | 8.7719 | 500 | 0.1443 | 0.9431 | 0.8587 | 0.8587 | 0.8587 |
| 0.1548 | 10.5263 | 600 | 0.1572 | 0.9387 | 0.8490 | 0.8416 | 0.8565 |
| 0.1802 | 12.2807 | 700 | 0.1436 | 0.9458 | 0.8656 | 0.8637 | 0.8675 |
| 0.1455 | 14.0351 | 800 | 0.1442 | 0.9467 | 0.8667 | 0.8725 | 0.8609 |
| 0.1514 | 15.7895 | 900 | 0.1500 | 0.9422 | 0.8571 | 0.8534 | 0.8609 |
| 0.1368 | 17.5439 | 1000 | 0.1391 | 0.9489 | 0.8718 | 0.8806 | 0.8631 |
| 0.1515 | 19.2982 | 1100 | 0.1370 | 0.9476 | 0.8700 | 0.8681 | 0.8720 |
| 0.1372 | 21.0526 | 1200 | 0.1393 | 0.9458 | 0.8644 | 0.8702 | 0.8587 |
| 0.1397 | 22.8070 | 1300 | 0.1359 | 0.9498 | 0.8746 | 0.8795 | 0.8698 |
| 0.1398 | 24.5614 | 1400 | 0.1352 | 0.9489 | 0.8740 | 0.8674 | 0.8808 |
| 0.1276 | 26.3158 | 1500 | 0.1381 | 0.9476 | 0.8700 | 0.8681 | 0.8720 |
| 0.1519 | 28.0702 | 1600 | 0.1380 | 0.9462 | 0.8666 | 0.8656 | 0.8675 |
| 0.1479 | 29.8246 | 1700 | 0.1343 | 0.9498 | 0.8754 | 0.8744 | 0.8764 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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