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---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224-pt22k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: beit-large-patch16-224-pt22k-finetuned-galaxy10-decals
results: []
---
<!-- 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. -->
# beit-large-patch16-224-pt22k-finetuned-galaxy10-decals
This model is a fine-tuned version of [microsoft/beit-large-patch16-224-pt22k](https://huggingface.co/microsoft/beit-large-patch16-224-pt22k) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5047
- Accuracy: 0.8771
- Precision: 0.8770
- Recall: 0.8771
- F1: 0.8764
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5632 | 0.99 | 62 | 1.3358 | 0.5265 | 0.5377 | 0.5265 | 0.4840 |
| 0.8801 | 2.0 | 125 | 0.7053 | 0.7717 | 0.7710 | 0.7717 | 0.7559 |
| 0.7408 | 2.99 | 187 | 0.5995 | 0.7897 | 0.7878 | 0.7897 | 0.7803 |
| 0.6124 | 4.0 | 250 | 0.5448 | 0.8140 | 0.8178 | 0.8140 | 0.8076 |
| 0.5799 | 4.99 | 312 | 0.5354 | 0.8174 | 0.8224 | 0.8174 | 0.8165 |
| 0.567 | 6.0 | 375 | 0.5044 | 0.8247 | 0.8314 | 0.8247 | 0.8194 |
| 0.5237 | 6.99 | 437 | 0.4913 | 0.8388 | 0.8429 | 0.8388 | 0.8371 |
| 0.4674 | 8.0 | 500 | 0.4927 | 0.8484 | 0.8541 | 0.8484 | 0.8477 |
| 0.4869 | 8.99 | 562 | 0.4167 | 0.8546 | 0.8570 | 0.8546 | 0.8526 |
| 0.4442 | 10.0 | 625 | 0.4086 | 0.8579 | 0.8583 | 0.8579 | 0.8564 |
| 0.4294 | 10.99 | 687 | 0.4743 | 0.8489 | 0.8516 | 0.8489 | 0.8489 |
| 0.4032 | 12.0 | 750 | 0.4350 | 0.8664 | 0.8651 | 0.8664 | 0.8647 |
| 0.4028 | 12.99 | 812 | 0.4443 | 0.8568 | 0.8623 | 0.8568 | 0.8561 |
| 0.3939 | 14.0 | 875 | 0.4193 | 0.8608 | 0.8605 | 0.8608 | 0.8593 |
| 0.3447 | 14.99 | 937 | 0.4289 | 0.8698 | 0.8692 | 0.8698 | 0.8688 |
| 0.354 | 16.0 | 1000 | 0.4471 | 0.8653 | 0.8661 | 0.8653 | 0.8648 |
| 0.2934 | 16.99 | 1062 | 0.4888 | 0.8574 | 0.8573 | 0.8574 | 0.8546 |
| 0.3262 | 18.0 | 1125 | 0.4605 | 0.8602 | 0.8602 | 0.8602 | 0.8588 |
| 0.3287 | 18.99 | 1187 | 0.4439 | 0.8681 | 0.8682 | 0.8681 | 0.8673 |
| 0.2848 | 20.0 | 1250 | 0.4986 | 0.8641 | 0.8633 | 0.8641 | 0.8615 |
| 0.283 | 20.99 | 1312 | 0.4663 | 0.8692 | 0.8681 | 0.8692 | 0.8676 |
| 0.3106 | 22.0 | 1375 | 0.4668 | 0.8720 | 0.8735 | 0.8720 | 0.8697 |
| 0.2785 | 22.99 | 1437 | 0.4899 | 0.8664 | 0.8649 | 0.8664 | 0.8650 |
| 0.2635 | 24.0 | 1500 | 0.5047 | 0.8771 | 0.8770 | 0.8771 | 0.8764 |
| 0.2573 | 24.99 | 1562 | 0.5144 | 0.8732 | 0.8730 | 0.8732 | 0.8723 |
| 0.238 | 26.0 | 1625 | 0.5012 | 0.8732 | 0.8729 | 0.8732 | 0.8723 |
| 0.2358 | 26.99 | 1687 | 0.5021 | 0.8681 | 0.8709 | 0.8681 | 0.8690 |
| 0.2624 | 28.0 | 1750 | 0.5154 | 0.8715 | 0.8711 | 0.8715 | 0.8705 |
| 0.229 | 28.99 | 1812 | 0.5087 | 0.8698 | 0.8690 | 0.8698 | 0.8689 |
| 0.227 | 29.76 | 1860 | 0.5104 | 0.8726 | 0.8725 | 0.8726 | 0.8718 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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