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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_rms_0001_fold1
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: 0.8497495826377296
---
<!-- 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_rms_0001_fold1
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: 1.4390
- Accuracy: 0.8497
## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8068 | 1.0 | 226 | 1.0605 | 0.3272 |
| 0.7543 | 2.0 | 452 | 0.8522 | 0.5943 |
| 0.6302 | 3.0 | 678 | 0.8188 | 0.6227 |
| 0.591 | 4.0 | 904 | 0.7284 | 0.6795 |
| 0.5365 | 5.0 | 1130 | 0.5240 | 0.7830 |
| 0.4875 | 6.0 | 1356 | 0.4715 | 0.8030 |
| 0.2956 | 7.0 | 1582 | 0.5230 | 0.8130 |
| 0.3385 | 8.0 | 1808 | 0.4637 | 0.8047 |
| 0.2498 | 9.0 | 2034 | 0.5733 | 0.8230 |
| 0.2375 | 10.0 | 2260 | 0.5001 | 0.8381 |
| 0.2383 | 11.0 | 2486 | 0.5213 | 0.8164 |
| 0.1638 | 12.0 | 2712 | 0.7500 | 0.8097 |
| 0.1669 | 13.0 | 2938 | 0.6347 | 0.8347 |
| 0.091 | 14.0 | 3164 | 0.8704 | 0.8164 |
| 0.0933 | 15.0 | 3390 | 0.6698 | 0.8280 |
| 0.1167 | 16.0 | 3616 | 0.7435 | 0.8481 |
| 0.0442 | 17.0 | 3842 | 0.8758 | 0.8164 |
| 0.0649 | 18.0 | 4068 | 0.8054 | 0.8247 |
| 0.0996 | 19.0 | 4294 | 0.8135 | 0.8164 |
| 0.0421 | 20.0 | 4520 | 0.8460 | 0.8464 |
| 0.0255 | 21.0 | 4746 | 1.2147 | 0.8097 |
| 0.0814 | 22.0 | 4972 | 0.8708 | 0.8331 |
| 0.07 | 23.0 | 5198 | 1.0564 | 0.8364 |
| 0.029 | 24.0 | 5424 | 1.0607 | 0.8364 |
| 0.0335 | 25.0 | 5650 | 1.0179 | 0.8464 |
| 0.0974 | 26.0 | 5876 | 0.8966 | 0.8364 |
| 0.0251 | 27.0 | 6102 | 1.0900 | 0.8297 |
| 0.0304 | 28.0 | 6328 | 0.9348 | 0.8347 |
| 0.0116 | 29.0 | 6554 | 1.0392 | 0.8447 |
| 0.036 | 30.0 | 6780 | 1.0080 | 0.8414 |
| 0.0176 | 31.0 | 7006 | 1.0131 | 0.8364 |
| 0.0187 | 32.0 | 7232 | 0.9626 | 0.8397 |
| 0.0495 | 33.0 | 7458 | 0.9911 | 0.8414 |
| 0.0106 | 34.0 | 7684 | 1.2195 | 0.8331 |
| 0.0005 | 35.0 | 7910 | 1.2232 | 0.8464 |
| 0.0148 | 36.0 | 8136 | 1.1060 | 0.8364 |
| 0.0093 | 37.0 | 8362 | 1.0552 | 0.8364 |
| 0.0212 | 38.0 | 8588 | 1.1910 | 0.8364 |
| 0.0009 | 39.0 | 8814 | 1.1001 | 0.8431 |
| 0.0083 | 40.0 | 9040 | 1.2874 | 0.8481 |
| 0.0296 | 41.0 | 9266 | 1.3495 | 0.8381 |
| 0.0225 | 42.0 | 9492 | 1.3683 | 0.8414 |
| 0.0158 | 43.0 | 9718 | 1.2852 | 0.8481 |
| 0.0056 | 44.0 | 9944 | 1.3620 | 0.8447 |
| 0.0126 | 45.0 | 10170 | 1.3137 | 0.8431 |
| 0.0 | 46.0 | 10396 | 1.4527 | 0.8497 |
| 0.013 | 47.0 | 10622 | 1.4028 | 0.8531 |
| 0.0375 | 48.0 | 10848 | 1.3979 | 0.8481 |
| 0.0006 | 49.0 | 11074 | 1.4369 | 0.8497 |
| 0.0135 | 50.0 | 11300 | 1.4390 | 0.8497 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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