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--- |
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license: apache-2.0 |
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base_model: facebook/dinov2-base-imagenet1k-1-layer |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: dinov2-base-imagenet1k-1-layer-head-finetuned-galaxy_mnist |
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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-base-imagenet1k-1-layer-head-finetuned-galaxy_mnist |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4438 |
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- Accuracy: 0.8385 |
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- Precision: 0.8388 |
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- Recall: 0.8385 |
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- F1: 0.8386 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.5682 | 0.99 | 31 | 1.4744 | 0.279 | 0.3518 | 0.279 | 0.2447 | |
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| 1.1895 | 1.98 | 62 | 1.1500 | 0.5175 | 0.5367 | 0.5175 | 0.5208 | |
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| 0.8876 | 2.98 | 93 | 0.8869 | 0.673 | 0.6822 | 0.673 | 0.6692 | |
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| 0.7366 | 4.0 | 125 | 0.7267 | 0.744 | 0.7472 | 0.744 | 0.7433 | |
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| 0.6499 | 4.99 | 156 | 0.6573 | 0.753 | 0.7566 | 0.753 | 0.7533 | |
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| 0.6147 | 5.98 | 187 | 0.6209 | 0.7695 | 0.7760 | 0.7695 | 0.7695 | |
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| 0.5911 | 6.98 | 218 | 0.5802 | 0.7855 | 0.7869 | 0.7855 | 0.7860 | |
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| 0.5489 | 8.0 | 250 | 0.5573 | 0.799 | 0.8019 | 0.799 | 0.7992 | |
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| 0.5306 | 8.99 | 281 | 0.5318 | 0.801 | 0.8005 | 0.801 | 0.8004 | |
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| 0.5154 | 9.98 | 312 | 0.5177 | 0.8095 | 0.8098 | 0.8095 | 0.8096 | |
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| 0.4963 | 10.98 | 343 | 0.5097 | 0.8155 | 0.8157 | 0.8155 | 0.8149 | |
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| 0.5304 | 12.0 | 375 | 0.4971 | 0.814 | 0.8144 | 0.814 | 0.8139 | |
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| 0.4779 | 12.99 | 406 | 0.4889 | 0.817 | 0.8182 | 0.817 | 0.8160 | |
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| 0.4774 | 13.98 | 437 | 0.4822 | 0.8245 | 0.8255 | 0.8245 | 0.8250 | |
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| 0.4852 | 14.98 | 468 | 0.4772 | 0.827 | 0.8290 | 0.827 | 0.8272 | |
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| 0.4693 | 16.0 | 500 | 0.4724 | 0.8285 | 0.8308 | 0.8285 | 0.8290 | |
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| 0.456 | 16.99 | 531 | 0.4636 | 0.829 | 0.8294 | 0.829 | 0.8289 | |
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| 0.4703 | 17.98 | 562 | 0.4623 | 0.829 | 0.8303 | 0.829 | 0.8291 | |
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| 0.4715 | 18.98 | 593 | 0.4609 | 0.8305 | 0.8329 | 0.8305 | 0.8308 | |
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| 0.4775 | 20.0 | 625 | 0.4574 | 0.835 | 0.8369 | 0.835 | 0.8353 | |
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| 0.4776 | 20.99 | 656 | 0.4531 | 0.833 | 0.8338 | 0.833 | 0.8332 | |
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| 0.4599 | 21.98 | 687 | 0.4500 | 0.8325 | 0.8333 | 0.8325 | 0.8325 | |
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| 0.4391 | 22.98 | 718 | 0.4510 | 0.832 | 0.8341 | 0.832 | 0.8321 | |
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| 0.4537 | 24.0 | 750 | 0.4466 | 0.835 | 0.8356 | 0.835 | 0.8352 | |
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| 0.4555 | 24.99 | 781 | 0.4451 | 0.8355 | 0.8365 | 0.8355 | 0.8356 | |
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| 0.4476 | 25.98 | 812 | 0.4438 | 0.8385 | 0.8388 | 0.8385 | 0.8386 | |
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| 0.4618 | 26.98 | 843 | 0.4440 | 0.8355 | 0.8366 | 0.8355 | 0.8358 | |
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| 0.4734 | 28.0 | 875 | 0.4432 | 0.835 | 0.8362 | 0.835 | 0.8352 | |
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| 0.4453 | 28.99 | 906 | 0.4433 | 0.8365 | 0.8378 | 0.8365 | 0.8367 | |
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| 0.4591 | 29.76 | 930 | 0.4430 | 0.8365 | 0.8376 | 0.8365 | 0.8367 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.1 |
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