---
base_model: apple/mobilevitv2-1.0-imagenet1k-256
datasets:
- webdataset
library_name: transformers
license: other
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: webdataset
      type: webdataset
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9444444444444444
      name: Accuracy
    - type: f1
      value: 0.8544819557625145
      name: F1
    - type: precision
      value: 0.8615023474178404
      name: Precision
    - type: recall
      value: 0.8475750577367206
      name: Recall
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost

This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1539
- Accuracy: 0.9444
- F1: 0.8545
- Precision: 0.8615
- Recall: 0.8476

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6635        | 1.7544  | 100  | 0.6513          | 0.7604   | 0.5705 | 0.4355    | 0.8268 |
| 0.4461        | 3.5088  | 200  | 0.3972          | 0.8769   | 0.7292 | 0.6322    | 0.8614 |
| 0.2599        | 5.2632  | 300  | 0.2404          | 0.9227   | 0.8049 | 0.7821    | 0.8291 |
| 0.2074        | 7.0175  | 400  | 0.1942          | 0.9347   | 0.8256 | 0.8488    | 0.8037 |
| 0.167         | 8.7719  | 500  | 0.1772          | 0.9364   | 0.8354 | 0.8326    | 0.8383 |
| 0.1661        | 10.5263 | 600  | 0.1653          | 0.9342   | 0.8259 | 0.8417    | 0.8106 |
| 0.1603        | 12.2807 | 700  | 0.1649          | 0.9409   | 0.8473 | 0.8425    | 0.8522 |
| 0.1523        | 14.0351 | 800  | 0.1568          | 0.9467   | 0.8592 | 0.8735    | 0.8453 |
| 0.1506        | 15.7895 | 900  | 0.1548          | 0.9431   | 0.8494 | 0.8657    | 0.8337 |
| 0.1485        | 17.5439 | 1000 | 0.1539          | 0.9444   | 0.8545 | 0.8615    | 0.8476 |
| 0.1263        | 19.2982 | 1100 | 0.1521          | 0.944    | 0.8535 | 0.8595    | 0.8476 |
| 0.1444        | 21.0526 | 1200 | 0.1552          | 0.9418   | 0.8471 | 0.8561    | 0.8383 |
| 0.1133        | 22.8070 | 1300 | 0.1531          | 0.9449   | 0.8561 | 0.8601    | 0.8522 |
| 0.1019        | 24.5614 | 1400 | 0.1577          | 0.9431   | 0.8491 | 0.8675    | 0.8314 |
| 0.1141        | 26.3158 | 1500 | 0.1560          | 0.9413   | 0.8472 | 0.8492    | 0.8453 |
| 0.1087        | 28.0702 | 1600 | 0.1573          | 0.9422   | 0.8492 | 0.8531    | 0.8453 |
| 0.1015        | 29.8246 | 1700 | 0.1545          | 0.9422   | 0.8488 | 0.8548    | 0.8430 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1