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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_adamax_00001_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: 0.9083333333333333
---
<!-- 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_3x_beit_base_adamax_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7465
- Accuracy: 0.9083
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2292 | 1.0 | 375 | 0.3140 | 0.8683 |
| 0.2093 | 2.0 | 750 | 0.2336 | 0.9067 |
| 0.1226 | 3.0 | 1125 | 0.2477 | 0.9117 |
| 0.0876 | 4.0 | 1500 | 0.2419 | 0.92 |
| 0.0679 | 5.0 | 1875 | 0.3315 | 0.8983 |
| 0.0323 | 6.0 | 2250 | 0.3578 | 0.9033 |
| 0.0877 | 7.0 | 2625 | 0.4065 | 0.91 |
| 0.0394 | 8.0 | 3000 | 0.4499 | 0.91 |
| 0.0357 | 9.0 | 3375 | 0.4692 | 0.9167 |
| 0.0126 | 10.0 | 3750 | 0.5130 | 0.9083 |
| 0.0533 | 11.0 | 4125 | 0.4610 | 0.915 |
| 0.018 | 12.0 | 4500 | 0.5545 | 0.9167 |
| 0.0014 | 13.0 | 4875 | 0.6258 | 0.905 |
| 0.0294 | 14.0 | 5250 | 0.5991 | 0.9133 |
| 0.0147 | 15.0 | 5625 | 0.5948 | 0.91 |
| 0.0096 | 16.0 | 6000 | 0.6032 | 0.905 |
| 0.03 | 17.0 | 6375 | 0.6625 | 0.9017 |
| 0.0009 | 18.0 | 6750 | 0.6142 | 0.9067 |
| 0.0073 | 19.0 | 7125 | 0.7447 | 0.8917 |
| 0.0002 | 20.0 | 7500 | 0.6954 | 0.9067 |
| 0.0056 | 21.0 | 7875 | 0.7051 | 0.9133 |
| 0.0361 | 22.0 | 8250 | 0.6457 | 0.9067 |
| 0.0618 | 23.0 | 8625 | 0.6671 | 0.905 |
| 0.0003 | 24.0 | 9000 | 0.7426 | 0.905 |
| 0.0152 | 25.0 | 9375 | 0.6683 | 0.905 |
| 0.0005 | 26.0 | 9750 | 0.7087 | 0.9067 |
| 0.0002 | 27.0 | 10125 | 0.7480 | 0.905 |
| 0.004 | 28.0 | 10500 | 0.7511 | 0.905 |
| 0.0094 | 29.0 | 10875 | 0.7080 | 0.9017 |
| 0.0056 | 30.0 | 11250 | 0.7631 | 0.905 |
| 0.0441 | 31.0 | 11625 | 0.7580 | 0.9083 |
| 0.0024 | 32.0 | 12000 | 0.7682 | 0.9083 |
| 0.0002 | 33.0 | 12375 | 0.7496 | 0.9133 |
| 0.0005 | 34.0 | 12750 | 0.7614 | 0.9067 |
| 0.0057 | 35.0 | 13125 | 0.7635 | 0.9083 |
| 0.0004 | 36.0 | 13500 | 0.7425 | 0.9117 |
| 0.0153 | 37.0 | 13875 | 0.7300 | 0.91 |
| 0.0003 | 38.0 | 14250 | 0.7331 | 0.9083 |
| 0.0149 | 39.0 | 14625 | 0.7175 | 0.905 |
| 0.0093 | 40.0 | 15000 | 0.7444 | 0.9067 |
| 0.0001 | 41.0 | 15375 | 0.7317 | 0.9117 |
| 0.0 | 42.0 | 15750 | 0.7474 | 0.9033 |
| 0.0002 | 43.0 | 16125 | 0.7578 | 0.905 |
| 0.0004 | 44.0 | 16500 | 0.7636 | 0.905 |
| 0.0 | 45.0 | 16875 | 0.7676 | 0.91 |
| 0.0005 | 46.0 | 17250 | 0.7589 | 0.91 |
| 0.0158 | 47.0 | 17625 | 0.7484 | 0.9083 |
| 0.0001 | 48.0 | 18000 | 0.7568 | 0.91 |
| 0.0001 | 49.0 | 18375 | 0.7501 | 0.9083 |
| 0.0109 | 50.0 | 18750 | 0.7465 | 0.9083 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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