transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_adamax_00001_fold5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_deit_base_adamax_00001_fold5

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6879
- Accuracy: 0.9

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2538        | 1.0   | 375   | 0.3339          | 0.8467   |
| 0.1892        | 2.0   | 750   | 0.2698          | 0.8917   |
| 0.1183        | 3.0   | 1125  | 0.2626          | 0.9      |
| 0.1141        | 4.0   | 1500  | 0.2917          | 0.8883   |
| 0.071         | 5.0   | 1875  | 0.3136          | 0.9      |
| 0.0217        | 6.0   | 2250  | 0.3618          | 0.8933   |
| 0.0346        | 7.0   | 2625  | 0.4213          | 0.89     |
| 0.0047        | 8.0   | 3000  | 0.4689          | 0.8983   |
| 0.022         | 9.0   | 3375  | 0.5039          | 0.8983   |
| 0.0024        | 10.0  | 3750  | 0.5358          | 0.8917   |
| 0.0059        | 11.0  | 4125  | 0.5640          | 0.895    |
| 0.0001        | 12.0  | 4500  | 0.5647          | 0.8967   |
| 0.0001        | 13.0  | 4875  | 0.6088          | 0.895    |
| 0.0002        | 14.0  | 5250  | 0.5907          | 0.9017   |
| 0.0001        | 15.0  | 5625  | 0.6332          | 0.8967   |
| 0.0001        | 16.0  | 6000  | 0.6424          | 0.8833   |
| 0.0001        | 17.0  | 6375  | 0.6207          | 0.8983   |
| 0.0068        | 18.0  | 6750  | 0.6552          | 0.895    |
| 0.0           | 19.0  | 7125  | 0.6642          | 0.9017   |
| 0.0           | 20.0  | 7500  | 0.6453          | 0.8883   |
| 0.0001        | 21.0  | 7875  | 0.6986          | 0.895    |
| 0.0265        | 22.0  | 8250  | 0.7065          | 0.8883   |
| 0.0           | 23.0  | 8625  | 0.6670          | 0.8967   |
| 0.0           | 24.0  | 9000  | 0.6793          | 0.8967   |
| 0.0           | 25.0  | 9375  | 0.6516          | 0.9017   |
| 0.0           | 26.0  | 9750  | 0.6626          | 0.89     |
| 0.0           | 27.0  | 10125 | 0.6877          | 0.895    |
| 0.0           | 28.0  | 10500 | 0.6598          | 0.8967   |
| 0.0           | 29.0  | 10875 | 0.6682          | 0.8933   |
| 0.0145        | 30.0  | 11250 | 0.6761          | 0.8983   |
| 0.0           | 31.0  | 11625 | 0.6763          | 0.8983   |
| 0.0           | 32.0  | 12000 | 0.6749          | 0.8967   |
| 0.0           | 33.0  | 12375 | 0.6798          | 0.8983   |
| 0.0           | 34.0  | 12750 | 0.6830          | 0.8967   |
| 0.0           | 35.0  | 13125 | 0.6787          | 0.9      |
| 0.0           | 36.0  | 13500 | 0.6883          | 0.895    |
| 0.0           | 37.0  | 13875 | 0.6805          | 0.8983   |
| 0.003         | 38.0  | 14250 | 0.6825          | 0.895    |
| 0.0           | 39.0  | 14625 | 0.6851          | 0.8967   |
| 0.0           | 40.0  | 15000 | 0.6877          | 0.8983   |
| 0.0           | 41.0  | 15375 | 0.6804          | 0.8983   |
| 0.0           | 42.0  | 15750 | 0.6888          | 0.8983   |
| 0.0           | 43.0  | 16125 | 0.6877          | 0.8983   |
| 0.0           | 44.0  | 16500 | 0.6899          | 0.8983   |
| 0.0           | 45.0  | 16875 | 0.6911          | 0.8967   |
| 0.0           | 46.0  | 17250 | 0.6870          | 0.8983   |
| 0.0023        | 47.0  | 17625 | 0.6868          | 0.8983   |
| 0.0           | 48.0  | 18000 | 0.6892          | 0.9      |
| 0.0           | 49.0  | 18375 | 0.6892          | 0.9      |
| 0.0021        | 50.0  | 18750 | 0.6879          | 0.9      |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
