---
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: 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: 0.9297777777777778
    - name: F1
      type: f1
      value: 0.8175519630484989
    - name: Precision
      type: precision
      value: 0.8119266055045872
    - name: Recall
      type: recall
      value: 0.8232558139534883
---

<!-- 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-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: 0.1708
- Accuracy: 0.9298
- F1: 0.8176
- Precision: 0.8119
- Recall: 0.8233

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6549        | 1.7544  | 100  | 0.6289          | 0.82     | 0.6260 | 0.5191    | 0.7884 |
| 0.4616        | 3.5088  | 200  | 0.4192          | 0.8867   | 0.7296 | 0.6706    | 0.8    |
| 0.3101        | 5.2632  | 300  | 0.3071          | 0.9036   | 0.7318 | 0.7810    | 0.6884 |
| 0.2932        | 7.0175  | 400  | 0.2486          | 0.908    | 0.7460 | 0.7896    | 0.7070 |
| 0.2652        | 8.7719  | 500  | 0.2279          | 0.9138   | 0.7674 | 0.7921    | 0.7442 |
| 0.2253        | 10.5263 | 600  | 0.2100          | 0.9218   | 0.7859 | 0.8240    | 0.7512 |
| 0.2257        | 12.2807 | 700  | 0.1951          | 0.9249   | 0.8019 | 0.8085    | 0.7953 |
| 0.2468        | 14.0351 | 800  | 0.1906          | 0.9307   | 0.8199 | 0.8142    | 0.8256 |
| 0.1796        | 15.7895 | 900  | 0.1949          | 0.9276   | 0.8120 | 0.8055    | 0.8186 |
| 0.1888        | 17.5439 | 1000 | 0.1807          | 0.9307   | 0.8178 | 0.8216    | 0.8140 |
| 0.202         | 19.2982 | 1100 | 0.1772          | 0.9342   | 0.8287 | 0.8249    | 0.8326 |
| 0.1824        | 21.0526 | 1200 | 0.1826          | 0.9276   | 0.8080 | 0.8186    | 0.7977 |
| 0.1808        | 22.8070 | 1300 | 0.1682          | 0.9347   | 0.8297 | 0.8268    | 0.8326 |
| 0.1792        | 24.5614 | 1400 | 0.1688          | 0.9364   | 0.8324 | 0.8392    | 0.8256 |
| 0.1852        | 26.3158 | 1500 | 0.1725          | 0.9338   | 0.8269 | 0.8260    | 0.8279 |
| 0.177         | 28.0702 | 1600 | 0.1690          | 0.9351   | 0.8282 | 0.8381    | 0.8186 |
| 0.1857        | 29.8246 | 1700 | 0.1708          | 0.9298   | 0.8176 | 0.8119    | 0.8233 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1