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---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_adamax_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.905
---
<!-- 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. -->
# smids_10x_beit_large_adamax_001_fold5
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8836
- Accuracy: 0.905
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3665 | 1.0 | 750 | 0.3594 | 0.8583 |
| 0.2964 | 2.0 | 1500 | 0.4126 | 0.8483 |
| 0.2817 | 3.0 | 2250 | 0.2955 | 0.895 |
| 0.2107 | 4.0 | 3000 | 0.4285 | 0.8483 |
| 0.2441 | 5.0 | 3750 | 0.2917 | 0.905 |
| 0.2284 | 6.0 | 4500 | 0.3000 | 0.8933 |
| 0.1417 | 7.0 | 5250 | 0.3775 | 0.9033 |
| 0.1212 | 8.0 | 6000 | 0.4010 | 0.9 |
| 0.1114 | 9.0 | 6750 | 0.3900 | 0.8917 |
| 0.1229 | 10.0 | 7500 | 0.5863 | 0.8833 |
| 0.0978 | 11.0 | 8250 | 0.5114 | 0.8883 |
| 0.019 | 12.0 | 9000 | 0.6596 | 0.9033 |
| 0.0244 | 13.0 | 9750 | 0.6428 | 0.9017 |
| 0.0242 | 14.0 | 10500 | 0.6293 | 0.9 |
| 0.0159 | 15.0 | 11250 | 0.5943 | 0.9067 |
| 0.0287 | 16.0 | 12000 | 0.4876 | 0.9033 |
| 0.0161 | 17.0 | 12750 | 0.7094 | 0.8933 |
| 0.0033 | 18.0 | 13500 | 0.7392 | 0.9117 |
| 0.0133 | 19.0 | 14250 | 0.6855 | 0.9017 |
| 0.0009 | 20.0 | 15000 | 0.7025 | 0.895 |
| 0.033 | 21.0 | 15750 | 0.5767 | 0.895 |
| 0.0007 | 22.0 | 16500 | 0.6533 | 0.8983 |
| 0.0005 | 23.0 | 17250 | 0.8501 | 0.8883 |
| 0.0041 | 24.0 | 18000 | 0.6751 | 0.91 |
| 0.0016 | 25.0 | 18750 | 0.8175 | 0.8983 |
| 0.022 | 26.0 | 19500 | 0.7166 | 0.9067 |
| 0.002 | 27.0 | 20250 | 0.7746 | 0.9033 |
| 0.0002 | 28.0 | 21000 | 0.7048 | 0.91 |
| 0.0002 | 29.0 | 21750 | 0.8217 | 0.9083 |
| 0.0187 | 30.0 | 22500 | 0.7107 | 0.8983 |
| 0.0002 | 31.0 | 23250 | 0.7863 | 0.9133 |
| 0.0 | 32.0 | 24000 | 0.8314 | 0.8983 |
| 0.0 | 33.0 | 24750 | 0.7909 | 0.8967 |
| 0.0003 | 34.0 | 25500 | 0.8566 | 0.905 |
| 0.0 | 35.0 | 26250 | 0.7280 | 0.9117 |
| 0.0 | 36.0 | 27000 | 0.8236 | 0.9017 |
| 0.0068 | 37.0 | 27750 | 0.7886 | 0.92 |
| 0.0 | 38.0 | 28500 | 0.8302 | 0.9017 |
| 0.0 | 39.0 | 29250 | 0.8589 | 0.9067 |
| 0.0 | 40.0 | 30000 | 0.8152 | 0.9017 |
| 0.0 | 41.0 | 30750 | 0.8501 | 0.905 |
| 0.0 | 42.0 | 31500 | 0.8563 | 0.91 |
| 0.0 | 43.0 | 32250 | 0.7690 | 0.9117 |
| 0.0 | 44.0 | 33000 | 0.8007 | 0.9083 |
| 0.0 | 45.0 | 33750 | 0.8622 | 0.9033 |
| 0.0001 | 46.0 | 34500 | 0.8624 | 0.905 |
| 0.0 | 47.0 | 35250 | 0.8665 | 0.9067 |
| 0.0 | 48.0 | 36000 | 0.8739 | 0.9067 |
| 0.0 | 49.0 | 36750 | 0.8825 | 0.9067 |
| 0.0 | 50.0 | 37500 | 0.8836 | 0.905 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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