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---
license: apache-2.0
base_model: google/vit-huge-patch14-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-huge-patch14-224-in21k-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-huge-patch14-224-in21k-finetuned-galaxy10-decals
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4927
- Accuracy: 0.8523
- Precision: 0.8538
- Recall: 0.8523
- F1: 0.8489
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7563 | 0.99 | 62 | 1.6011 | 0.5096 | 0.4694 | 0.5096 | 0.4415 |
| 1.0516 | 2.0 | 125 | 0.9115 | 0.7661 | 0.7679 | 0.7661 | 0.7525 |
| 0.8551 | 2.99 | 187 | 0.7590 | 0.7706 | 0.7860 | 0.7706 | 0.7653 |
| 0.6701 | 4.0 | 250 | 0.6253 | 0.8095 | 0.8013 | 0.8095 | 0.7985 |
| 0.6112 | 4.99 | 312 | 0.6058 | 0.8095 | 0.8120 | 0.8095 | 0.8083 |
| 0.6109 | 6.0 | 375 | 0.5428 | 0.8292 | 0.8353 | 0.8292 | 0.8196 |
| 0.5643 | 6.99 | 437 | 0.5230 | 0.8343 | 0.8350 | 0.8343 | 0.8332 |
| 0.5204 | 8.0 | 500 | 0.5010 | 0.8365 | 0.8391 | 0.8365 | 0.8344 |
| 0.4918 | 8.99 | 562 | 0.5000 | 0.8365 | 0.8419 | 0.8365 | 0.8348 |
| 0.4673 | 10.0 | 625 | 0.4949 | 0.8410 | 0.8394 | 0.8410 | 0.8371 |
| 0.4569 | 10.99 | 687 | 0.4803 | 0.8467 | 0.8451 | 0.8467 | 0.8446 |
| 0.4164 | 12.0 | 750 | 0.5012 | 0.8326 | 0.8314 | 0.8326 | 0.8295 |
| 0.424 | 12.99 | 812 | 0.4940 | 0.8410 | 0.8454 | 0.8410 | 0.8382 |
| 0.4045 | 14.0 | 875 | 0.4927 | 0.8523 | 0.8538 | 0.8523 | 0.8489 |
| 0.3651 | 14.99 | 937 | 0.4809 | 0.8416 | 0.8396 | 0.8416 | 0.8403 |
| 0.3512 | 16.0 | 1000 | 0.4955 | 0.8331 | 0.8306 | 0.8331 | 0.8307 |
| 0.2922 | 16.99 | 1062 | 0.5103 | 0.8399 | 0.8357 | 0.8399 | 0.8359 |
| 0.3212 | 18.0 | 1125 | 0.5197 | 0.8439 | 0.8408 | 0.8439 | 0.8412 |
| 0.3171 | 18.99 | 1187 | 0.5253 | 0.8348 | 0.8335 | 0.8348 | 0.8335 |
| 0.2896 | 20.0 | 1250 | 0.5303 | 0.8467 | 0.8456 | 0.8467 | 0.8438 |
| 0.271 | 20.99 | 1312 | 0.5571 | 0.8393 | 0.8391 | 0.8393 | 0.8366 |
| 0.2996 | 22.0 | 1375 | 0.5468 | 0.8422 | 0.8411 | 0.8422 | 0.8404 |
| 0.2663 | 22.99 | 1437 | 0.5620 | 0.8405 | 0.8393 | 0.8405 | 0.8393 |
| 0.2513 | 24.0 | 1500 | 0.5338 | 0.8467 | 0.8448 | 0.8467 | 0.8450 |
| 0.2453 | 24.99 | 1562 | 0.5562 | 0.8484 | 0.8452 | 0.8484 | 0.8446 |
| 0.2237 | 26.0 | 1625 | 0.5619 | 0.8467 | 0.8450 | 0.8467 | 0.8442 |
| 0.2296 | 26.99 | 1687 | 0.5751 | 0.8484 | 0.8496 | 0.8484 | 0.8464 |
| 0.2479 | 28.0 | 1750 | 0.5782 | 0.8461 | 0.8441 | 0.8461 | 0.8431 |
| 0.2207 | 28.99 | 1812 | 0.5746 | 0.8410 | 0.8381 | 0.8410 | 0.8378 |
| 0.2125 | 29.76 | 1860 | 0.5754 | 0.8416 | 0.8393 | 0.8416 | 0.8383 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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