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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sign-language-classification
results: []
---
<!-- 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. -->
# sign-language-classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1351
- Accuracy: 0.96
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 32
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6016 | 1.0 | 100 | 1.5038 | 0.8 |
| 1.1072 | 2.0 | 200 | 0.6959 | 0.8675 |
| 0.6195 | 3.0 | 300 | 0.5236 | 0.87 |
| 0.5559 | 4.0 | 400 | 0.4819 | 0.87 |
| 0.389 | 5.0 | 500 | 0.3392 | 0.9 |
| 0.3878 | 6.0 | 600 | 0.3600 | 0.9025 |
| 0.3309 | 7.0 | 700 | 0.3312 | 0.9075 |
| 0.3397 | 8.0 | 800 | 0.2596 | 0.9225 |
| 0.3033 | 9.0 | 900 | 0.2056 | 0.935 |
| 0.2765 | 10.0 | 1000 | 0.2802 | 0.9175 |
| 0.2846 | 11.0 | 1100 | 0.3276 | 0.9025 |
| 0.2443 | 12.0 | 1200 | 0.3689 | 0.8975 |
| 0.2682 | 13.0 | 1300 | 0.2805 | 0.915 |
| 0.2053 | 14.0 | 1400 | 0.2437 | 0.9225 |
| 0.2453 | 15.0 | 1500 | 0.2646 | 0.92 |
| 0.1896 | 16.0 | 1600 | 0.2489 | 0.925 |
| 0.1841 | 17.0 | 1700 | 0.2393 | 0.9275 |
| 0.1406 | 18.0 | 1800 | 0.1935 | 0.945 |
| 0.1573 | 19.0 | 1900 | 0.2544 | 0.92 |
| 0.155 | 20.0 | 2000 | 0.1940 | 0.9475 |
| 0.1563 | 21.0 | 2100 | 0.2021 | 0.9325 |
| 0.133 | 22.0 | 2200 | 0.2413 | 0.9325 |
| 0.117 | 23.0 | 2300 | 0.1939 | 0.9375 |
| 0.1455 | 24.0 | 2400 | 0.1685 | 0.9575 |
| 0.144 | 25.0 | 2500 | 0.1787 | 0.9475 |
| 0.1119 | 26.0 | 2600 | 0.1511 | 0.96 |
| 0.1053 | 27.0 | 2700 | 0.1308 | 0.965 |
| 0.0964 | 28.0 | 2800 | 0.1042 | 0.9725 |
| 0.0938 | 29.0 | 2900 | 0.1751 | 0.9425 |
| 0.0881 | 30.0 | 3000 | 0.1066 | 0.965 |
| 0.0854 | 31.0 | 3100 | 0.1116 | 0.97 |
| 0.1002 | 32.0 | 3200 | 0.1351 | 0.96 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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