---
base_model: apple/mobilevitv2-1.0-imagenet1k-256
datasets:
- webdataset
license: other
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-frost
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: webdataset
      type: webdataset
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9309734513274336
      name: Accuracy
    - type: f1
      value: 0.8227272727272726
      name: F1
    - type: precision
      value: 0.8457943925233645
      name: Precision
    - type: recall
      value: 0.8008849557522124
      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. -->

# mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-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: 0.1896
- Accuracy: 0.9310
- F1: 0.8227
- Precision: 0.8458
- Recall: 0.8009

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6687        | 1.5625  | 100  | 0.6623          | 0.7230   | 0.5335 | 0.4022    | 0.7920 |
| 0.4454        | 3.125   | 200  | 0.4152          | 0.8832   | 0.7490 | 0.6567    | 0.8717 |
| 0.2835        | 4.6875  | 300  | 0.2661          | 0.9097   | 0.7661 | 0.7952    | 0.7389 |
| 0.2197        | 6.25    | 400  | 0.2151          | 0.9195   | 0.7869 | 0.8358    | 0.7434 |
| 0.1613        | 7.8125  | 500  | 0.2007          | 0.9292   | 0.8140 | 0.8578    | 0.7743 |
| 0.1655        | 9.375   | 600  | 0.1935          | 0.9310   | 0.8227 | 0.8458    | 0.8009 |
| 0.1815        | 10.9375 | 700  | 0.1883          | 0.9265   | 0.8074 | 0.8488    | 0.7699 |
| 0.1316        | 12.5    | 800  | 0.1825          | 0.9327   | 0.8273 | 0.8505    | 0.8053 |
| 0.1612        | 14.0625 | 900  | 0.1837          | 0.9257   | 0.8100 | 0.8287    | 0.7920 |
| 0.118         | 15.625  | 1000 | 0.1896          | 0.9310   | 0.8227 | 0.8458    | 0.8009 |
| 0.1178        | 17.1875 | 1100 | 0.1937          | 0.9239   | 0.8028 | 0.8333    | 0.7743 |
| 0.1248        | 18.75   | 1200 | 0.1913          | 0.9301   | 0.8192 | 0.8483    | 0.7920 |
| 0.1169        | 20.3125 | 1300 | 0.1916          | 0.9301   | 0.8167 | 0.8585    | 0.7788 |
| 0.1094        | 21.875  | 1400 | 0.1925          | 0.9292   | 0.8182 | 0.8411    | 0.7965 |
| 0.1108        | 23.4375 | 1500 | 0.1961          | 0.9345   | 0.8333 | 0.8486    | 0.8186 |
| 0.1089        | 25.0    | 1600 | 0.1993          | 0.9283   | 0.8172 | 0.8341    | 0.8009 |
| 0.0919        | 26.5625 | 1700 | 0.1936          | 0.9319   | 0.8262 | 0.8433    | 0.8097 |
| 0.0969        | 28.125  | 1800 | 0.1978          | 0.9310   | 0.8227 | 0.8458    | 0.8009 |
| 0.1093        | 29.6875 | 1900 | 0.1955          | 0.9283   | 0.8172 | 0.8341    | 0.8009 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1