---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: sashes_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9393939393939394
---

<!-- 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. -->

# sashes_model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1902
- Accuracy: 0.9394

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 112

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.7273  | 2    | 1.1089          | 0.2909   |
| No log        | 1.8182  | 5    | 1.0961          | 0.3758   |
| No log        | 2.9091  | 8    | 1.0822          | 0.3758   |
| 1.0988        | 4.0     | 11   | 1.0480          | 0.5939   |
| 1.0988        | 4.7273  | 13   | 1.0333          | 0.6061   |
| 1.0988        | 5.8182  | 16   | 0.9881          | 0.6667   |
| 1.0988        | 6.9091  | 19   | 0.9274          | 0.6970   |
| 1.0061        | 8.0     | 22   | 0.8714          | 0.8364   |
| 1.0061        | 8.7273  | 24   | 0.8210          | 0.7879   |
| 1.0061        | 9.8182  | 27   | 0.7619          | 0.8121   |
| 0.8136        | 10.9091 | 30   | 0.6483          | 0.8667   |
| 0.8136        | 12.0    | 33   | 0.6937          | 0.7939   |
| 0.8136        | 12.7273 | 35   | 0.5885          | 0.8545   |
| 0.8136        | 13.8182 | 38   | 0.6046          | 0.8182   |
| 0.642         | 14.9091 | 41   | 0.5518          | 0.8364   |
| 0.642         | 16.0    | 44   | 0.5370          | 0.8242   |
| 0.642         | 16.7273 | 46   | 0.4765          | 0.8788   |
| 0.642         | 17.8182 | 49   | 0.4416          | 0.8606   |
| 0.5145        | 18.9091 | 52   | 0.4140          | 0.8970   |
| 0.5145        | 20.0    | 55   | 0.4007          | 0.8970   |
| 0.5145        | 20.7273 | 57   | 0.3803          | 0.8970   |
| 0.4226        | 21.8182 | 60   | 0.3167          | 0.9394   |
| 0.4226        | 22.9091 | 63   | 0.3398          | 0.9030   |
| 0.4226        | 24.0    | 66   | 0.3147          | 0.9273   |
| 0.4226        | 24.7273 | 68   | 0.3273          | 0.8970   |
| 0.3282        | 25.8182 | 71   | 0.3125          | 0.9030   |
| 0.3282        | 26.9091 | 74   | 0.2712          | 0.9212   |
| 0.3282        | 28.0    | 77   | 0.2871          | 0.9273   |
| 0.3282        | 28.7273 | 79   | 0.2534          | 0.9273   |
| 0.3076        | 29.8182 | 82   | 0.2620          | 0.9273   |
| 0.3076        | 30.9091 | 85   | 0.3845          | 0.8848   |
| 0.3076        | 32.0    | 88   | 0.2495          | 0.9273   |
| 0.3081        | 32.7273 | 90   | 0.3018          | 0.9091   |
| 0.3081        | 33.8182 | 93   | 0.2204          | 0.9455   |
| 0.3081        | 34.9091 | 96   | 0.2769          | 0.9152   |
| 0.3081        | 36.0    | 99   | 0.2261          | 0.9394   |
| 0.2451        | 36.7273 | 101  | 0.2092          | 0.9515   |
| 0.2451        | 37.8182 | 104  | 0.3196          | 0.8727   |
| 0.2451        | 38.9091 | 107  | 0.2629          | 0.9091   |
| 0.2741        | 40.0    | 110  | 0.2360          | 0.9333   |
| 0.2741        | 40.7273 | 112  | 0.1927          | 0.9515   |
| 0.2741        | 41.8182 | 115  | 0.2834          | 0.9030   |
| 0.2741        | 42.9091 | 118  | 0.2173          | 0.9394   |
| 0.244         | 44.0    | 121  | 0.1997          | 0.9394   |
| 0.244         | 44.7273 | 123  | 0.2163          | 0.9273   |
| 0.244         | 45.8182 | 126  | 0.2865          | 0.8970   |
| 0.244         | 46.9091 | 129  | 0.2483          | 0.9152   |
| 0.224         | 48.0    | 132  | 0.1707          | 0.9576   |
| 0.224         | 48.7273 | 134  | 0.1988          | 0.9455   |
| 0.224         | 49.8182 | 137  | 0.2168          | 0.9455   |
| 0.213         | 50.9091 | 140  | 0.1807          | 0.9576   |
| 0.213         | 52.0    | 143  | 0.2478          | 0.9152   |
| 0.213         | 52.7273 | 145  | 0.1975          | 0.9455   |
| 0.213         | 53.8182 | 148  | 0.2218          | 0.9212   |
| 0.2298        | 54.9091 | 151  | 0.2046          | 0.9455   |
| 0.2298        | 56.0    | 154  | 0.2557          | 0.9152   |
| 0.2298        | 56.7273 | 156  | 0.1962          | 0.9394   |
| 0.2298        | 57.8182 | 159  | 0.1879          | 0.9394   |
| 0.2189        | 58.9091 | 162  | 0.1983          | 0.9576   |
| 0.2189        | 60.0    | 165  | 0.1285          | 0.9697   |
| 0.2189        | 60.7273 | 167  | 0.2227          | 0.9212   |
| 0.211         | 61.8182 | 170  | 0.1671          | 0.9515   |
| 0.211         | 62.9091 | 173  | 0.1489          | 0.9636   |
| 0.211         | 64.0    | 176  | 0.1842          | 0.9394   |
| 0.211         | 64.7273 | 178  | 0.1687          | 0.9636   |
| 0.1834        | 65.8182 | 181  | 0.2118          | 0.9091   |
| 0.1834        | 66.9091 | 184  | 0.2191          | 0.9273   |
| 0.1834        | 68.0    | 187  | 0.2014          | 0.9273   |
| 0.1834        | 68.7273 | 189  | 0.1861          | 0.9515   |
| 0.1846        | 69.8182 | 192  | 0.1309          | 0.9758   |
| 0.1846        | 70.9091 | 195  | 0.1236          | 0.9636   |
| 0.1846        | 72.0    | 198  | 0.1541          | 0.9455   |
| 0.1581        | 72.7273 | 200  | 0.1577          | 0.9576   |
| 0.1581        | 73.8182 | 203  | 0.1927          | 0.9273   |
| 0.1581        | 74.9091 | 206  | 0.2247          | 0.9273   |
| 0.1581        | 76.0    | 209  | 0.1811          | 0.9576   |
| 0.1742        | 76.7273 | 211  | 0.2190          | 0.9273   |
| 0.1742        | 77.8182 | 214  | 0.1487          | 0.9697   |
| 0.1742        | 78.9091 | 217  | 0.1836          | 0.9576   |
| 0.1837        | 80.0    | 220  | 0.1228          | 0.9758   |
| 0.1837        | 80.7273 | 222  | 0.1400          | 0.9636   |
| 0.1837        | 81.4545 | 224  | 0.1902          | 0.9394   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1