transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_001_fold2
  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: 0.8685524126455907
---

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# smids_5x_deit_tiny_sgd_001_fold2

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3358
- Accuracy: 0.8686

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7754        | 1.0   | 375   | 0.7240          | 0.7271   |
| 0.5298        | 2.0   | 750   | 0.5482          | 0.7837   |
| 0.4453        | 3.0   | 1125  | 0.4761          | 0.8186   |
| 0.3233        | 4.0   | 1500  | 0.4354          | 0.8286   |
| 0.3301        | 5.0   | 1875  | 0.4115          | 0.8386   |
| 0.3179        | 6.0   | 2250  | 0.3924          | 0.8469   |
| 0.3101        | 7.0   | 2625  | 0.3803          | 0.8502   |
| 0.3266        | 8.0   | 3000  | 0.3685          | 0.8586   |
| 0.2663        | 9.0   | 3375  | 0.3605          | 0.8552   |
| 0.2805        | 10.0  | 3750  | 0.3550          | 0.8536   |
| 0.2677        | 11.0  | 4125  | 0.3495          | 0.8619   |
| 0.3046        | 12.0  | 4500  | 0.3461          | 0.8686   |
| 0.2173        | 13.0  | 4875  | 0.3409          | 0.8602   |
| 0.2384        | 14.0  | 5250  | 0.3398          | 0.8636   |
| 0.2681        | 15.0  | 5625  | 0.3343          | 0.8652   |
| 0.1901        | 16.0  | 6000  | 0.3336          | 0.8735   |
| 0.2623        | 17.0  | 6375  | 0.3353          | 0.8735   |
| 0.1865        | 18.0  | 6750  | 0.3314          | 0.8735   |
| 0.2003        | 19.0  | 7125  | 0.3309          | 0.8735   |
| 0.2713        | 20.0  | 7500  | 0.3280          | 0.8752   |
| 0.2017        | 21.0  | 7875  | 0.3298          | 0.8702   |
| 0.1863        | 22.0  | 8250  | 0.3281          | 0.8769   |
| 0.227         | 23.0  | 8625  | 0.3271          | 0.8769   |
| 0.1889        | 24.0  | 9000  | 0.3290          | 0.8752   |
| 0.1561        | 25.0  | 9375  | 0.3282          | 0.8752   |
| 0.2339        | 26.0  | 9750  | 0.3258          | 0.8752   |
| 0.2006        | 27.0  | 10125 | 0.3286          | 0.8802   |
| 0.1745        | 28.0  | 10500 | 0.3294          | 0.8719   |
| 0.1852        | 29.0  | 10875 | 0.3284          | 0.8719   |
| 0.1931        | 30.0  | 11250 | 0.3301          | 0.8702   |
| 0.1811        | 31.0  | 11625 | 0.3297          | 0.8735   |
| 0.1783        | 32.0  | 12000 | 0.3325          | 0.8702   |
| 0.1809        | 33.0  | 12375 | 0.3288          | 0.8769   |
| 0.1274        | 34.0  | 12750 | 0.3315          | 0.8652   |
| 0.1957        | 35.0  | 13125 | 0.3314          | 0.8702   |
| 0.1704        | 36.0  | 13500 | 0.3319          | 0.8686   |
| 0.1796        | 37.0  | 13875 | 0.3309          | 0.8686   |
| 0.1565        | 38.0  | 14250 | 0.3327          | 0.8702   |
| 0.1735        | 39.0  | 14625 | 0.3325          | 0.8686   |
| 0.1525        | 40.0  | 15000 | 0.3345          | 0.8669   |
| 0.1548        | 41.0  | 15375 | 0.3344          | 0.8735   |
| 0.1677        | 42.0  | 15750 | 0.3353          | 0.8669   |
| 0.1708        | 43.0  | 16125 | 0.3357          | 0.8669   |
| 0.1467        | 44.0  | 16500 | 0.3356          | 0.8669   |
| 0.1338        | 45.0  | 16875 | 0.3358          | 0.8686   |
| 0.2032        | 46.0  | 17250 | 0.3360          | 0.8669   |
| 0.1609        | 47.0  | 17625 | 0.3359          | 0.8686   |
| 0.155         | 48.0  | 18000 | 0.3359          | 0.8686   |
| 0.2258        | 49.0  | 18375 | 0.3359          | 0.8669   |
| 0.1319        | 50.0  | 18750 | 0.3358          | 0.8686   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
