|
--- |
|
license: apache-2.0 |
|
base_model: google/vit-base-patch16-224-in21k |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: asl_aplhabet_img_classifier |
|
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. --> |
|
|
|
# asl_aplhabet_img_classifier |
|
|
|
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: 2.9586 |
|
- Accuracy: 0.2692 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 26 | 3.2666 | 0.0385 | |
|
| No log | 2.0 | 52 | 3.2701 | 0.0385 | |
|
| No log | 3.0 | 78 | 3.2713 | 0.0288 | |
|
| No log | 4.0 | 104 | 3.2701 | 0.0769 | |
|
| No log | 5.0 | 130 | 3.2584 | 0.0385 | |
|
| No log | 6.0 | 156 | 3.2537 | 0.0577 | |
|
| No log | 7.0 | 182 | 3.2402 | 0.0577 | |
|
| No log | 8.0 | 208 | 3.2364 | 0.0577 | |
|
| No log | 9.0 | 234 | 3.2055 | 0.0769 | |
|
| No log | 10.0 | 260 | 3.1794 | 0.0769 | |
|
| No log | 11.0 | 286 | 3.1851 | 0.1346 | |
|
| No log | 12.0 | 312 | 3.1811 | 0.1058 | |
|
| No log | 13.0 | 338 | 3.1594 | 0.1346 | |
|
| No log | 14.0 | 364 | 3.1269 | 0.1635 | |
|
| No log | 15.0 | 390 | 3.1082 | 0.125 | |
|
| No log | 16.0 | 416 | 3.1019 | 0.2019 | |
|
| No log | 17.0 | 442 | 3.0886 | 0.2019 | |
|
| No log | 18.0 | 468 | 3.0599 | 0.2115 | |
|
| No log | 19.0 | 494 | 3.0622 | 0.1731 | |
|
| 3.0197 | 20.0 | 520 | 3.0474 | 0.1538 | |
|
| 3.0197 | 21.0 | 546 | 3.0245 | 0.2115 | |
|
| 3.0197 | 22.0 | 572 | 3.0386 | 0.1923 | |
|
| 3.0197 | 23.0 | 598 | 3.0236 | 0.1923 | |
|
| 3.0197 | 24.0 | 624 | 3.0201 | 0.1923 | |
|
| 3.0197 | 25.0 | 650 | 3.0056 | 0.2212 | |
|
| 3.0197 | 26.0 | 676 | 2.9649 | 0.25 | |
|
| 3.0197 | 27.0 | 702 | 2.9900 | 0.2212 | |
|
| 3.0197 | 28.0 | 728 | 2.9823 | 0.2308 | |
|
| 3.0197 | 29.0 | 754 | 2.9782 | 0.2115 | |
|
| 3.0197 | 30.0 | 780 | 3.0136 | 0.1635 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
