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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- webdataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: frost-vision-v2-google_vit-base-patch16-384-v2024-11-10
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: 0.9274647887323944
- name: F1
type: f1
value: 0.8186619718309859
- name: Precision
type: precision
value: 0.8172231985940246
- name: Recall
type: recall
value: 0.8201058201058201
---
<!-- 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-patch16-384-v2024-11-10
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1847
- Accuracy: 0.9275
- F1: 0.8187
- Precision: 0.8172
- Recall: 0.8201
## 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: 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2243 | 1.4085 | 100 | 0.2088 | 0.9243 | 0.7981 | 0.8534 | 0.7496 |
| 0.2438 | 2.8169 | 200 | 0.1819 | 0.9299 | 0.8103 | 0.8817 | 0.7496 |
| 0.1338 | 4.2254 | 300 | 0.1608 | 0.9377 | 0.8449 | 0.8397 | 0.8501 |
| 0.1224 | 5.6338 | 400 | 0.1735 | 0.9271 | 0.8179 | 0.8158 | 0.8201 |
| 0.1065 | 7.0423 | 500 | 0.1847 | 0.9275 | 0.8187 | 0.8172 | 0.8201 |
| 0.1008 | 8.4507 | 600 | 0.1710 | 0.9405 | 0.8506 | 0.8528 | 0.8483 |
| 0.1005 | 9.8592 | 700 | 0.1823 | 0.9384 | 0.8405 | 0.8698 | 0.8131 |
| 0.0756 | 11.2676 | 800 | 0.1771 | 0.9415 | 0.8520 | 0.8613 | 0.8430 |
| 0.0653 | 12.6761 | 900 | 0.1971 | 0.9324 | 0.8310 | 0.8295 | 0.8325 |
| 0.0367 | 14.0845 | 1000 | 0.2123 | 0.9296 | 0.8221 | 0.8294 | 0.8148 |
| 0.0459 | 15.4930 | 1100 | 0.2006 | 0.9335 | 0.832 | 0.8387 | 0.8254 |
| 0.0559 | 16.9014 | 1200 | 0.2097 | 0.9313 | 0.8232 | 0.8470 | 0.8007 |
| 0.0382 | 18.3099 | 1300 | 0.2055 | 0.9352 | 0.8372 | 0.8401 | 0.8342 |
| 0.0361 | 19.7183 | 1400 | 0.2070 | 0.9335 | 0.8305 | 0.8449 | 0.8166 |
| 0.0358 | 21.1268 | 1500 | 0.1959 | 0.9398 | 0.8458 | 0.8653 | 0.8272 |
| 0.0382 | 22.5352 | 1600 | 0.2097 | 0.9320 | 0.8269 | 0.8412 | 0.8131 |
| 0.0285 | 23.9437 | 1700 | 0.2016 | 0.9415 | 0.8515 | 0.8639 | 0.8395 |
| 0.0141 | 25.3521 | 1800 | 0.2161 | 0.9366 | 0.8384 | 0.8537 | 0.8236 |
| 0.0179 | 26.7606 | 1900 | 0.2073 | 0.9377 | 0.8427 | 0.8495 | 0.8360 |
| 0.0263 | 28.1690 | 2000 | 0.2097 | 0.9391 | 0.8457 | 0.8556 | 0.8360 |
| 0.0191 | 29.5775 | 2100 | 0.2101 | 0.9377 | 0.8415 | 0.8545 | 0.8289 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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