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license: apache-2.0
base_model: facebook/dinov2-base-imagenet1k-1-layer
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: dinov2-base-imagenet1k-1-layer-finetuned-galaxy_mnist
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. -->
# dinov2-base-imagenet1k-1-layer-finetuned-galaxy_mnist
This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-base-imagenet1k-1-layer) on the matthieulel/galaxy_mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1875
- Accuracy: 0.9365
- Precision: 0.9367
- Recall: 0.9365
- F1: 0.9365
## 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-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8401 | 0.99 | 31 | 0.6220 | 0.7605 | 0.7579 | 0.7605 | 0.7543 |
| 0.3857 | 1.98 | 62 | 0.2696 | 0.8875 | 0.8881 | 0.8875 | 0.8877 |
| 0.3144 | 2.98 | 93 | 0.2491 | 0.9015 | 0.9028 | 0.9015 | 0.9010 |
| 0.2769 | 4.0 | 125 | 0.2179 | 0.913 | 0.9129 | 0.913 | 0.9128 |
| 0.2858 | 4.99 | 156 | 0.2455 | 0.9025 | 0.9070 | 0.9025 | 0.9020 |
| 0.2704 | 5.98 | 187 | 0.2121 | 0.9155 | 0.9234 | 0.9155 | 0.9156 |
| 0.2557 | 6.98 | 218 | 0.2177 | 0.9155 | 0.9190 | 0.9155 | 0.9152 |
| 0.2069 | 8.0 | 250 | 0.1864 | 0.9255 | 0.9256 | 0.9255 | 0.9255 |
| 0.2344 | 8.99 | 281 | 0.1894 | 0.923 | 0.9237 | 0.923 | 0.9230 |
| 0.1996 | 9.98 | 312 | 0.1993 | 0.9235 | 0.9260 | 0.9235 | 0.9234 |
| 0.2011 | 10.98 | 343 | 0.1828 | 0.928 | 0.9280 | 0.928 | 0.9279 |
| 0.2229 | 12.0 | 375 | 0.2358 | 0.9155 | 0.9233 | 0.9155 | 0.9145 |
| 0.1792 | 12.99 | 406 | 0.1897 | 0.9205 | 0.9214 | 0.9205 | 0.9205 |
| 0.1898 | 13.98 | 437 | 0.2017 | 0.921 | 0.9217 | 0.921 | 0.9208 |
| 0.1735 | 14.98 | 468 | 0.1954 | 0.927 | 0.9270 | 0.927 | 0.9269 |
| 0.1751 | 16.0 | 500 | 0.1918 | 0.9295 | 0.9299 | 0.9295 | 0.9294 |
| 0.1732 | 16.99 | 531 | 0.1906 | 0.922 | 0.9225 | 0.922 | 0.9219 |
| 0.1738 | 17.98 | 562 | 0.1846 | 0.931 | 0.9317 | 0.931 | 0.9310 |
| 0.1694 | 18.98 | 593 | 0.1875 | 0.9365 | 0.9367 | 0.9365 | 0.9365 |
| 0.1723 | 20.0 | 625 | 0.1941 | 0.9285 | 0.9293 | 0.9285 | 0.9284 |
| 0.1574 | 20.99 | 656 | 0.1905 | 0.9335 | 0.9337 | 0.9335 | 0.9336 |
| 0.1485 | 21.98 | 687 | 0.1869 | 0.9315 | 0.9313 | 0.9315 | 0.9314 |
| 0.1537 | 22.98 | 718 | 0.1830 | 0.936 | 0.9360 | 0.936 | 0.9360 |
| 0.1406 | 24.0 | 750 | 0.1975 | 0.932 | 0.9322 | 0.932 | 0.9320 |
| 0.1326 | 24.99 | 781 | 0.1918 | 0.9315 | 0.9316 | 0.9315 | 0.9315 |
| 0.1238 | 25.98 | 812 | 0.2105 | 0.9275 | 0.9288 | 0.9275 | 0.9276 |
| 0.1299 | 26.98 | 843 | 0.2022 | 0.9325 | 0.9327 | 0.9325 | 0.9324 |
| 0.1387 | 28.0 | 875 | 0.2011 | 0.9335 | 0.9337 | 0.9335 | 0.9336 |
| 0.1279 | 28.99 | 906 | 0.2005 | 0.931 | 0.9310 | 0.931 | 0.9310 |
| 0.1256 | 29.76 | 930 | 0.2004 | 0.931 | 0.9310 | 0.931 | 0.9310 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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