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---
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-large-patch16-224-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. -->
# vit-large-patch16-224-finetuned-galaxy10-decals
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Accuracy: 0.8551
- Precision: 0.8525
- Recall: 0.8551
- F1: 0.8526
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0358 | 0.99 | 31 | 0.7598 | 0.7362 | 0.7425 | 0.7362 | 0.7266 |
| 0.6822 | 1.98 | 62 | 0.6136 | 0.7920 | 0.7972 | 0.7920 | 0.7899 |
| 0.6535 | 2.98 | 93 | 0.5416 | 0.8106 | 0.8140 | 0.8106 | 0.8062 |
| 0.5696 | 4.0 | 125 | 0.5305 | 0.8162 | 0.8195 | 0.8162 | 0.8140 |
| 0.5435 | 4.99 | 156 | 0.5555 | 0.8145 | 0.8242 | 0.8145 | 0.8161 |
| 0.4621 | 5.98 | 187 | 0.5075 | 0.8298 | 0.8344 | 0.8298 | 0.8254 |
| 0.4479 | 6.98 | 218 | 0.5118 | 0.8281 | 0.8291 | 0.8281 | 0.8269 |
| 0.4318 | 8.0 | 250 | 0.5164 | 0.8196 | 0.8255 | 0.8196 | 0.8166 |
| 0.4011 | 8.99 | 281 | 0.5087 | 0.8410 | 0.8369 | 0.8410 | 0.8362 |
| 0.355 | 9.98 | 312 | 0.5063 | 0.8410 | 0.8433 | 0.8410 | 0.8405 |
| 0.3655 | 10.98 | 343 | 0.5419 | 0.8326 | 0.8343 | 0.8326 | 0.8305 |
| 0.3292 | 12.0 | 375 | 0.5134 | 0.8439 | 0.8442 | 0.8439 | 0.8415 |
| 0.3207 | 12.99 | 406 | 0.6285 | 0.8185 | 0.8293 | 0.8185 | 0.8178 |
| 0.2931 | 13.98 | 437 | 0.5627 | 0.8382 | 0.8395 | 0.8382 | 0.8371 |
| 0.2817 | 14.98 | 468 | 0.6059 | 0.8207 | 0.8278 | 0.8207 | 0.8215 |
| 0.2713 | 16.0 | 500 | 0.6140 | 0.8382 | 0.8367 | 0.8382 | 0.8337 |
| 0.233 | 16.99 | 531 | 0.5992 | 0.8382 | 0.8384 | 0.8382 | 0.8374 |
| 0.2313 | 17.98 | 562 | 0.6679 | 0.8292 | 0.8343 | 0.8292 | 0.8278 |
| 0.223 | 18.98 | 593 | 0.6501 | 0.8343 | 0.8386 | 0.8343 | 0.8347 |
| 0.2126 | 20.0 | 625 | 0.6731 | 0.8343 | 0.8304 | 0.8343 | 0.8296 |
| 0.2078 | 20.99 | 656 | 0.6335 | 0.8388 | 0.8410 | 0.8388 | 0.8383 |
| 0.201 | 21.98 | 687 | 0.6120 | 0.8506 | 0.8478 | 0.8506 | 0.8485 |
| 0.2045 | 22.98 | 718 | 0.6590 | 0.8410 | 0.8389 | 0.8410 | 0.8371 |
| 0.1759 | 24.0 | 750 | 0.6478 | 0.8489 | 0.8464 | 0.8489 | 0.8457 |
| 0.1856 | 24.99 | 781 | 0.6604 | 0.8444 | 0.8413 | 0.8444 | 0.8420 |
| 0.1766 | 25.98 | 812 | 0.6922 | 0.8501 | 0.8491 | 0.8501 | 0.8484 |
| 0.1841 | 26.98 | 843 | 0.6485 | 0.8501 | 0.8493 | 0.8501 | 0.8486 |
| 0.1707 | 28.0 | 875 | 0.6393 | 0.8467 | 0.8440 | 0.8467 | 0.8446 |
| 0.1792 | 28.99 | 906 | 0.6404 | 0.8551 | 0.8525 | 0.8551 | 0.8526 |
| 0.1713 | 29.76 | 930 | 0.6398 | 0.8534 | 0.8513 | 0.8534 | 0.8511 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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