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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- webdataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: frost-vision-v2-google_vit-base-patch16-224-v2024-11-14
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: webdataset
type: webdataset
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9388888888888889
- name: F1
type: f1
value: 0.8436018957345972
- name: Precision
type: precision
value: 0.8654781199351702
- name: Recall
type: recall
value: 0.8228043143297381
---
<!-- 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. -->
# frost-vision-v2-google_vit-base-patch16-224-v2024-11-14
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.9389
- F1: 0.8436
- Precision: 0.8655
- Recall: 0.8228
## 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: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3381 | 1.2346 | 100 | 0.3271 | 0.8660 | 0.5669 | 0.8045 | 0.4376 |
| 0.2067 | 2.4691 | 200 | 0.2080 | 0.9194 | 0.7827 | 0.8514 | 0.7242 |
| 0.1745 | 3.7037 | 300 | 0.1864 | 0.9228 | 0.8003 | 0.8308 | 0.7720 |
| 0.1724 | 4.9383 | 400 | 0.1792 | 0.9299 | 0.8188 | 0.8493 | 0.7904 |
| 0.128 | 6.1728 | 500 | 0.1736 | 0.9327 | 0.8292 | 0.8437 | 0.8151 |
| 0.1034 | 7.4074 | 600 | 0.1672 | 0.9355 | 0.8348 | 0.8571 | 0.8136 |
| 0.0944 | 8.6420 | 700 | 0.1579 | 0.9392 | 0.8452 | 0.8622 | 0.8290 |
| 0.0919 | 9.8765 | 800 | 0.1631 | 0.9364 | 0.8347 | 0.8710 | 0.8012 |
| 0.0791 | 11.1111 | 900 | 0.1592 | 0.9380 | 0.8383 | 0.8771 | 0.8028 |
| 0.0684 | 12.3457 | 1000 | 0.1577 | 0.9389 | 0.8436 | 0.8655 | 0.8228 |
| 0.0737 | 13.5802 | 1100 | 0.1678 | 0.9380 | 0.8416 | 0.8613 | 0.8228 |
| 0.0625 | 14.8148 | 1200 | 0.1646 | 0.9426 | 0.8542 | 0.8692 | 0.8398 |
| 0.0591 | 16.0494 | 1300 | 0.1625 | 0.9432 | 0.8549 | 0.8756 | 0.8351 |
| 0.0464 | 17.2840 | 1400 | 0.1722 | 0.9386 | 0.8422 | 0.8676 | 0.8182 |
| 0.048 | 18.5185 | 1500 | 0.1694 | 0.9401 | 0.8472 | 0.8663 | 0.8290 |
| 0.0353 | 19.7531 | 1600 | 0.1715 | 0.9392 | 0.8462 | 0.8576 | 0.8351 |
| 0.0434 | 20.9877 | 1700 | 0.1817 | 0.9370 | 0.8386 | 0.8618 | 0.8166 |
| 0.0332 | 22.2222 | 1800 | 0.1797 | 0.9383 | 0.8423 | 0.8627 | 0.8228 |
| 0.0283 | 23.4568 | 1900 | 0.1810 | 0.9401 | 0.8482 | 0.8617 | 0.8351 |
| 0.0474 | 24.6914 | 2000 | 0.1765 | 0.9398 | 0.8454 | 0.8709 | 0.8213 |
| 0.0365 | 25.9259 | 2100 | 0.1835 | 0.9414 | 0.8516 | 0.8637 | 0.8398 |
| 0.0244 | 27.1605 | 2200 | 0.1822 | 0.9404 | 0.8479 | 0.8677 | 0.8290 |
| 0.0242 | 28.3951 | 2300 | 0.1808 | 0.9407 | 0.8483 | 0.8703 | 0.8274 |
| 0.0296 | 29.6296 | 2400 | 0.1817 | 0.9401 | 0.8477 | 0.864 | 0.8320 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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