---
base_model: apple/mobilevit-xx-small
datasets:
- webdataset
library_name: transformers
license: other
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: frost-mobile-apple__mobilevit-xx-small-v2024-10-22
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: webdataset
      type: webdataset
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9475555555555556
      name: Accuracy
    - type: f1
      value: 0.8700440528634361
      name: F1
    - type: precision
      value: 0.8681318681318682
      name: Precision
    - type: recall
      value: 0.8719646799116998
      name: Recall
---

<!-- 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: 0.1381
- Accuracy: 0.9476
- F1: 0.8700
- Precision: 0.8681
- Recall: 0.8720

## 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