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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_5x_beit_base_adamax_001_fold3
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: 0.835
---
<!-- 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_5x_beit_base_adamax_001_fold3
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: 1.1469
- Accuracy: 0.835
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8795 | 1.0 | 375 | 1.0709 | 0.465 |
| 0.805 | 2.0 | 750 | 0.8267 | 0.54 |
| 0.7578 | 3.0 | 1125 | 0.8592 | 0.5683 |
| 1.0023 | 4.0 | 1500 | 0.7631 | 0.6317 |
| 0.7622 | 5.0 | 1875 | 0.6997 | 0.685 |
| 0.5711 | 6.0 | 2250 | 0.5607 | 0.76 |
| 0.5125 | 7.0 | 2625 | 0.4986 | 0.8067 |
| 0.5239 | 8.0 | 3000 | 0.4781 | 0.8 |
| 0.4547 | 9.0 | 3375 | 0.6145 | 0.77 |
| 0.4777 | 10.0 | 3750 | 0.4360 | 0.8267 |
| 0.3636 | 11.0 | 4125 | 0.4074 | 0.8417 |
| 0.4518 | 12.0 | 4500 | 0.4481 | 0.8317 |
| 0.3493 | 13.0 | 4875 | 0.5307 | 0.805 |
| 0.3009 | 14.0 | 5250 | 0.4470 | 0.835 |
| 0.2927 | 15.0 | 5625 | 0.4302 | 0.8383 |
| 0.233 | 16.0 | 6000 | 0.4642 | 0.835 |
| 0.3176 | 17.0 | 6375 | 0.4522 | 0.835 |
| 0.2704 | 18.0 | 6750 | 0.4353 | 0.8317 |
| 0.2544 | 19.0 | 7125 | 0.4509 | 0.835 |
| 0.2122 | 20.0 | 7500 | 0.5169 | 0.8183 |
| 0.135 | 21.0 | 7875 | 0.5912 | 0.82 |
| 0.1564 | 22.0 | 8250 | 0.4970 | 0.8383 |
| 0.2284 | 23.0 | 8625 | 0.5113 | 0.8217 |
| 0.1624 | 24.0 | 9000 | 0.6295 | 0.825 |
| 0.165 | 25.0 | 9375 | 0.5951 | 0.81 |
| 0.0933 | 26.0 | 9750 | 0.6337 | 0.8233 |
| 0.1787 | 27.0 | 10125 | 0.5739 | 0.8267 |
| 0.0977 | 28.0 | 10500 | 0.6837 | 0.8283 |
| 0.0607 | 29.0 | 10875 | 0.7084 | 0.8467 |
| 0.0515 | 30.0 | 11250 | 0.8096 | 0.8167 |
| 0.0587 | 31.0 | 11625 | 0.8299 | 0.8367 |
| 0.1097 | 32.0 | 12000 | 0.7487 | 0.8333 |
| 0.0784 | 33.0 | 12375 | 0.7788 | 0.815 |
| 0.0505 | 34.0 | 12750 | 0.8520 | 0.8417 |
| 0.0243 | 35.0 | 13125 | 0.8865 | 0.8233 |
| 0.0517 | 36.0 | 13500 | 0.8229 | 0.83 |
| 0.0484 | 37.0 | 13875 | 0.9870 | 0.8367 |
| 0.0198 | 38.0 | 14250 | 0.9718 | 0.825 |
| 0.0203 | 39.0 | 14625 | 0.8284 | 0.8467 |
| 0.0211 | 40.0 | 15000 | 0.9506 | 0.8333 |
| 0.0035 | 41.0 | 15375 | 0.9695 | 0.8367 |
| 0.0109 | 42.0 | 15750 | 1.1050 | 0.835 |
| 0.0054 | 43.0 | 16125 | 1.1815 | 0.8317 |
| 0.0043 | 44.0 | 16500 | 1.0406 | 0.8433 |
| 0.0242 | 45.0 | 16875 | 1.1360 | 0.8417 |
| 0.0127 | 46.0 | 17250 | 1.1706 | 0.8317 |
| 0.0068 | 47.0 | 17625 | 1.1596 | 0.8333 |
| 0.0108 | 48.0 | 18000 | 1.1303 | 0.8333 |
| 0.0029 | 49.0 | 18375 | 1.1332 | 0.8267 |
| 0.0113 | 50.0 | 18750 | 1.1469 | 0.835 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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