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README.md
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---
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license: apache-2.0
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base_model: facebook/dinov2-giant
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: dinov2-giant-2024_01_02-kornia_img-size518_batch-size32_epochs20_freeze
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# dinov2-giant-2024_01_02-kornia_img-size518_batch-size32_epochs20_freeze
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This model is a fine-tuned version of [facebook/dinov2-giant](https://huggingface.co/facebook/dinov2-giant) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1276
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- F1 Micro: 0.8134
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- F1 Macro: 0.7677
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- Roc Auc: 0.8759
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- Accuracy: 0.5263
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- Learning Rate: 0.001
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.01
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Accuracy | F1 Macro | F1 Micro | Validation Loss | Roc Auc | Rate |
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|:-------------:|:-----:|:----:|:--------:|:--------:|:--------:|:---------------:|:-------:|:-----:|
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| No log | 1.0 | 268 | 0.4055 | 0.5114 | 0.6258 | 0.2231 | 0.7463 | 0.01 |
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| 0.2273 | 2.0 | 536 | 0.3812 | 0.4511 | 0.6106 | 0.2505 | 0.7360 | 0.01 |
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| 0.2273 | 3.0 | 804 | 0.4176 | 0.6952 | 0.7531 | 0.1782 | 0.8425 | 0.01 |
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| 0.196 | 4.0 | 1072 | 0.4241 | 0.6667 | 0.7646 | 0.1578 | 0.8562 | 0.01 |
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| 0.196 | 5.0 | 1340 | 0.3551 | 0.6463 | 0.7290 | 0.1978 | 0.8616 | 0.01 |
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| 0.1916 | 6.0 | 1608 | 0.4548 | 0.6155 | 0.7534 | 0.1570 | 0.8332 | 0.01 |
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| 0.1916 | 7.0 | 1876 | 0.4076 | 0.7034 | 0.7711 | 0.1704 | 0.8893 | 0.01 |
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| 0.1935 | 8.0 | 2144 | 0.4487 | 0.7240 | 0.7783 | 0.1584 | 0.8759 | 0.01 |
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| 0.1935 | 9.0 | 2412 | 0.4434 | 0.7026 | 0.7725 | 0.1614 | 0.8787 | 0.01 |
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| 0.1945 | 10.0 | 2680 | 0.4366 | 0.6245 | 0.7438 | 0.1569 | 0.8239 | 0.01 |
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| 0.1945 | 11.0 | 2948 | 0.4298 | 0.6986 | 0.7639 | 0.1666 | 0.8614 | 0.01 |
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| 0.1951 | 12.0 | 3216 | 0.4477 | 0.6291 | 0.7448 | 0.1585 | 0.8242 | 0.01 |
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| 0.1951 | 13.0 | 3484 | 0.1565 | 0.7624 | 0.6650 | 0.8443 | 0.4380 | 0.01 |
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| 0.1953 | 14.0 | 3752 | 0.1728 | 0.6728 | 0.5022 | 0.7639 | 0.4466 | 0.01 |
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| 0.1945 | 15.0 | 4020 | 0.1565 | 0.7441 | 0.6524 | 0.8177 | 0.4555 | 0.01 |
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| 0.1945 | 16.0 | 4288 | 0.1576 | 0.7515 | 0.6439 | 0.8311 | 0.4580 | 0.01 |
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| 0.1929 | 17.0 | 4556 | 0.1701 | 0.7359 | 0.5707 | 0.8337 | 0.4312 | 0.01 |
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| 0.1929 | 18.0 | 4824 | 0.1599 | 0.7531 | 0.6534 | 0.8451 | 0.4230 | 0.01 |
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| 0.1952 | 19.0 | 5092 | 0.1603 | 0.7347 | 0.6658 | 0.8118 | 0.4548 | 0.01 |
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| 0.1952 | 20.0 | 5360 | 0.1276 | 0.8134 | 0.7677 | 0.8759 | 0.5263 | 0.001 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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