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---
base_model: google/vit-base-patch16-224-in21k
datasets:
- webdataset
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: webdataset
type: webdataset
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.963716814159292
name: Accuracy
- type: f1
value: 0.9118279569892475
name: F1
- type: precision
value: 0.905982905982906
name: Precision
- type: recall
value: 0.9177489177489178
name: Recall
---
<!-- 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. -->
# vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0965
- Accuracy: 0.9637
- F1: 0.9118
- Precision: 0.9060
- Recall: 0.9177
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0728 | 1.5625 | 100 | 0.0659 | 0.9841 | 0.9607 | 0.9692 | 0.9524 |
| 0.0871 | 3.125 | 200 | 0.1244 | 0.9566 | 0.8942 | 0.8922 | 0.8961 |
| 0.0999 | 4.6875 | 300 | 0.1043 | 0.9637 | 0.9126 | 0.8992 | 0.9264 |
| 0.0743 | 6.25 | 400 | 0.1043 | 0.9611 | 0.9043 | 0.9083 | 0.9004 |
| 0.0655 | 7.8125 | 500 | 0.0965 | 0.9637 | 0.9118 | 0.9060 | 0.9177 |
| 0.0559 | 9.375 | 600 | 0.1038 | 0.9619 | 0.9087 | 0.8917 | 0.9264 |
| 0.0517 | 10.9375 | 700 | 0.0972 | 0.9584 | 0.8998 | 0.8866 | 0.9134 |
| 0.0407 | 12.5 | 800 | 0.1120 | 0.9637 | 0.9111 | 0.9130 | 0.9091 |
| 0.0513 | 14.0625 | 900 | 0.1093 | 0.9558 | 0.8894 | 0.9095 | 0.8701 |
| 0.0378 | 15.625 | 1000 | 0.1197 | 0.9549 | 0.8889 | 0.8947 | 0.8831 |
| 0.0487 | 17.1875 | 1100 | 0.0955 | 0.9646 | 0.9138 | 0.9099 | 0.9177 |
| 0.0272 | 18.75 | 1200 | 0.1088 | 0.9566 | 0.8928 | 0.9027 | 0.8831 |
| 0.0241 | 20.3125 | 1300 | 0.0979 | 0.9637 | 0.9114 | 0.9095 | 0.9134 |
| 0.0311 | 21.875 | 1400 | 0.1134 | 0.9655 | 0.9158 | 0.9138 | 0.9177 |
| 0.0303 | 23.4375 | 1500 | 0.1092 | 0.9628 | 0.9079 | 0.92 | 0.8961 |
| 0.0225 | 25.0 | 1600 | 0.1121 | 0.9628 | 0.9083 | 0.9163 | 0.9004 |
| 0.0292 | 26.5625 | 1700 | 0.1149 | 0.9619 | 0.9071 | 0.9052 | 0.9091 |
| 0.0261 | 28.125 | 1800 | 0.1107 | 0.9619 | 0.9079 | 0.8983 | 0.9177 |
| 0.0166 | 29.6875 | 1900 | 0.1110 | 0.9611 | 0.9052 | 0.9013 | 0.9091 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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