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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-v2024-11-11
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: 0.9320422535211268
- name: F1
type: f1
value: 0.8224471021159153
- name: Precision
type: precision
value: 0.8171846435100548
- name: Recall
type: recall
value: 0.8277777777777777
---
<!-- 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-v2024-11-11
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: 0.1658
- Accuracy: 0.9320
- F1: 0.8224
- Precision: 0.8172
- Recall: 0.8278
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3127 | 1.4085 | 100 | 0.2932 | 0.8940 | 0.6725 | 0.8153 | 0.5722 |
| 0.193 | 2.8169 | 200 | 0.2136 | 0.9190 | 0.7834 | 0.7969 | 0.7704 |
| 0.1503 | 4.2254 | 300 | 0.1815 | 0.9278 | 0.8100 | 0.8108 | 0.8093 |
| 0.1313 | 5.6338 | 400 | 0.1623 | 0.9327 | 0.8183 | 0.8415 | 0.7963 |
| 0.1166 | 7.0423 | 500 | 0.1658 | 0.9320 | 0.8224 | 0.8172 | 0.8278 |
| 0.093 | 8.4507 | 600 | 0.1606 | 0.9384 | 0.8405 | 0.8276 | 0.8537 |
| 0.0931 | 9.8592 | 700 | 0.1625 | 0.9366 | 0.8370 | 0.8191 | 0.8556 |
| 0.0733 | 11.2676 | 800 | 0.1714 | 0.9356 | 0.8310 | 0.8287 | 0.8333 |
| 0.0693 | 12.6761 | 900 | 0.1568 | 0.9398 | 0.8403 | 0.8475 | 0.8333 |
| 0.0615 | 14.0845 | 1000 | 0.1666 | 0.9342 | 0.8270 | 0.8262 | 0.8278 |
| 0.0562 | 15.4930 | 1100 | 0.1636 | 0.9394 | 0.8404 | 0.8420 | 0.8389 |
| 0.0507 | 16.9014 | 1200 | 0.1613 | 0.9401 | 0.8435 | 0.8388 | 0.8481 |
| 0.0552 | 18.3099 | 1300 | 0.1590 | 0.9412 | 0.8455 | 0.8447 | 0.8463 |
| 0.0439 | 19.7183 | 1400 | 0.1704 | 0.9394 | 0.8425 | 0.8333 | 0.8519 |
| 0.0367 | 21.1268 | 1500 | 0.1702 | 0.9426 | 0.8484 | 0.8523 | 0.8444 |
| 0.0424 | 22.5352 | 1600 | 0.1685 | 0.9394 | 0.8419 | 0.8358 | 0.8481 |
| 0.0306 | 23.9437 | 1700 | 0.1771 | 0.9380 | 0.8397 | 0.8262 | 0.8537 |
| 0.0352 | 25.3521 | 1800 | 0.1691 | 0.9401 | 0.8440 | 0.8364 | 0.8519 |
| 0.0323 | 26.7606 | 1900 | 0.1687 | 0.9426 | 0.8509 | 0.8409 | 0.8611 |
| 0.0297 | 28.1690 | 2000 | 0.1732 | 0.9401 | 0.8455 | 0.8304 | 0.8611 |
| 0.0229 | 29.5775 | 2100 | 0.1712 | 0.9412 | 0.8475 | 0.8360 | 0.8593 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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