transformers tensorboard safetensors 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_1x_deit_small_sgd_0001_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: 0.7054908485856906
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_1x_deit_small_sgd_0001_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: 0.7825
- Accuracy: 0.7055

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0783        | 1.0   | 75   | 1.0542          | 0.4509   |
| 1.0523        | 2.0   | 150  | 1.0379          | 0.4892   |
| 1.0294        | 3.0   | 225  | 1.0237          | 0.5225   |
| 1.0365        | 4.0   | 300  | 1.0109          | 0.5391   |
| 1.0245        | 5.0   | 375  | 0.9996          | 0.5524   |
| 0.9804        | 6.0   | 450  | 0.9885          | 0.5674   |
| 1.0004        | 7.0   | 525  | 0.9780          | 0.5857   |
| 0.979         | 8.0   | 600  | 0.9674          | 0.5957   |
| 0.9543        | 9.0   | 675  | 0.9573          | 0.5973   |
| 0.9564        | 10.0  | 750  | 0.9477          | 0.6023   |
| 0.9543        | 11.0  | 825  | 0.9383          | 0.6190   |
| 0.9466        | 12.0  | 900  | 0.9293          | 0.6256   |
| 0.9322        | 13.0  | 975  | 0.9204          | 0.6273   |
| 0.8987        | 14.0  | 1050 | 0.9121          | 0.6356   |
| 0.9075        | 15.0  | 1125 | 0.9040          | 0.6356   |
| 0.9086        | 16.0  | 1200 | 0.8961          | 0.6456   |
| 0.8923        | 17.0  | 1275 | 0.8885          | 0.6473   |
| 0.8806        | 18.0  | 1350 | 0.8814          | 0.6506   |
| 0.8958        | 19.0  | 1425 | 0.8744          | 0.6522   |
| 0.8609        | 20.0  | 1500 | 0.8676          | 0.6572   |
| 0.9085        | 21.0  | 1575 | 0.8614          | 0.6656   |
| 0.8274        | 22.0  | 1650 | 0.8554          | 0.6689   |
| 0.8481        | 23.0  | 1725 | 0.8495          | 0.6739   |
| 0.8248        | 24.0  | 1800 | 0.8441          | 0.6739   |
| 0.8342        | 25.0  | 1875 | 0.8389          | 0.6722   |
| 0.8564        | 26.0  | 1950 | 0.8341          | 0.6805   |
| 0.8458        | 27.0  | 2025 | 0.8294          | 0.6822   |
| 0.7955        | 28.0  | 2100 | 0.8250          | 0.6839   |
| 0.8045        | 29.0  | 2175 | 0.8208          | 0.6839   |
| 0.8063        | 30.0  | 2250 | 0.8168          | 0.6822   |
| 0.8139        | 31.0  | 2325 | 0.8131          | 0.6855   |
| 0.8204        | 32.0  | 2400 | 0.8097          | 0.6855   |
| 0.7688        | 33.0  | 2475 | 0.8065          | 0.6889   |
| 0.8146        | 34.0  | 2550 | 0.8035          | 0.6938   |
| 0.7717        | 35.0  | 2625 | 0.8006          | 0.6938   |
| 0.7969        | 36.0  | 2700 | 0.7981          | 0.6955   |
| 0.805         | 37.0  | 2775 | 0.7957          | 0.6955   |
| 0.8385        | 38.0  | 2850 | 0.7936          | 0.6988   |
| 0.7682        | 39.0  | 2925 | 0.7916          | 0.6988   |
| 0.7759        | 40.0  | 3000 | 0.7898          | 0.7005   |
| 0.8019        | 41.0  | 3075 | 0.7883          | 0.7005   |
| 0.7801        | 42.0  | 3150 | 0.7869          | 0.7005   |
| 0.7773        | 43.0  | 3225 | 0.7857          | 0.6988   |
| 0.788         | 44.0  | 3300 | 0.7847          | 0.7005   |
| 0.7811        | 45.0  | 3375 | 0.7839          | 0.7022   |
| 0.7761        | 46.0  | 3450 | 0.7833          | 0.7022   |
| 0.7855        | 47.0  | 3525 | 0.7828          | 0.7038   |
| 0.7857        | 48.0  | 3600 | 0.7826          | 0.7055   |
| 0.7597        | 49.0  | 3675 | 0.7825          | 0.7055   |
| 0.7828        | 50.0  | 3750 | 0.7825          | 0.7055   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
