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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Chess_Images
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: 0.9333333333333333
---
<!-- 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. -->
# Chess_Images
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: 0.2460
- Accuracy: 0.9333
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.3365 | 0.9333 |
| No log | 2.0 | 4 | 0.3018 | 0.9333 |
| No log | 3.0 | 6 | 0.3443 | 0.9667 |
| No log | 4.0 | 8 | 0.2189 | 1.0 |
| 0.213 | 5.0 | 10 | 0.3188 | 0.9667 |
| 0.213 | 6.0 | 12 | 0.2903 | 0.9333 |
| 0.213 | 7.0 | 14 | 0.3398 | 0.9 |
| 0.213 | 8.0 | 16 | 0.3879 | 0.8667 |
| 0.213 | 9.0 | 18 | 0.3023 | 0.9333 |
| 0.2116 | 10.0 | 20 | 0.1857 | 1.0 |
| 0.2116 | 11.0 | 22 | 0.2737 | 0.9667 |
| 0.2116 | 12.0 | 24 | 0.2675 | 1.0 |
| 0.2116 | 13.0 | 26 | 0.2817 | 0.9333 |
| 0.2116 | 14.0 | 28 | 0.4394 | 0.8667 |
| 0.1837 | 15.0 | 30 | 0.3167 | 0.9 |
| 0.1837 | 16.0 | 32 | 0.2795 | 0.9333 |
| 0.1837 | 17.0 | 34 | 0.2315 | 0.9333 |
| 0.1837 | 18.0 | 36 | 0.2266 | 0.9667 |
| 0.1837 | 19.0 | 38 | 0.3199 | 0.9333 |
| 0.1726 | 20.0 | 40 | 0.2553 | 0.9667 |
| 0.1726 | 21.0 | 42 | 0.3804 | 0.9 |
| 0.1726 | 22.0 | 44 | 0.2118 | 0.9667 |
| 0.1726 | 23.0 | 46 | 0.1784 | 1.0 |
| 0.1726 | 24.0 | 48 | 0.2098 | 0.9667 |
| 0.1529 | 25.0 | 50 | 0.1676 | 1.0 |
| 0.1529 | 26.0 | 52 | 0.2980 | 0.9 |
| 0.1529 | 27.0 | 54 | 0.2726 | 0.9667 |
| 0.1529 | 28.0 | 56 | 0.1756 | 1.0 |
| 0.1529 | 29.0 | 58 | 0.2266 | 0.9667 |
| 0.1335 | 30.0 | 60 | 0.3161 | 0.9333 |
| 0.1335 | 31.0 | 62 | 0.2872 | 0.9333 |
| 0.1335 | 32.0 | 64 | 0.2030 | 1.0 |
| 0.1335 | 33.0 | 66 | 0.2297 | 0.9333 |
| 0.1335 | 34.0 | 68 | 0.2876 | 0.9333 |
| 0.1228 | 35.0 | 70 | 0.1432 | 1.0 |
| 0.1228 | 36.0 | 72 | 0.2194 | 0.9667 |
| 0.1228 | 37.0 | 74 | 0.1387 | 1.0 |
| 0.1228 | 38.0 | 76 | 0.1381 | 1.0 |
| 0.1228 | 39.0 | 78 | 0.1540 | 1.0 |
| 0.1324 | 40.0 | 80 | 0.3075 | 0.8667 |
| 0.1324 | 41.0 | 82 | 0.1892 | 1.0 |
| 0.1324 | 42.0 | 84 | 0.1487 | 1.0 |
| 0.1324 | 43.0 | 86 | 0.1515 | 1.0 |
| 0.1324 | 44.0 | 88 | 0.2617 | 0.9333 |
| 0.136 | 45.0 | 90 | 0.1719 | 0.9667 |
| 0.136 | 46.0 | 92 | 0.2501 | 0.9 |
| 0.136 | 47.0 | 94 | 0.1618 | 1.0 |
| 0.136 | 48.0 | 96 | 0.2175 | 0.9667 |
| 0.136 | 49.0 | 98 | 0.2039 | 0.9667 |
| 0.1226 | 50.0 | 100 | 0.2460 | 0.9333 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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