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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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: banknote18k |
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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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# banknote18k |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0096 |
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- Accuracy: 0.9987 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.4947 | 0.12 | 100 | 0.3407 | 0.9451 | |
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| 0.423 | 0.23 | 200 | 0.2200 | 0.9451 | |
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| 0.2237 | 0.35 | 300 | 0.1613 | 0.9536 | |
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| 0.2806 | 0.46 | 400 | 0.0884 | 0.9810 | |
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| 0.1188 | 0.58 | 500 | 0.0512 | 0.9895 | |
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| 0.3279 | 0.7 | 600 | 0.0568 | 0.9876 | |
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| 0.1054 | 0.81 | 700 | 0.0342 | 0.9928 | |
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| 0.0924 | 0.93 | 800 | 0.0536 | 0.9863 | |
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| 0.1068 | 1.05 | 900 | 0.0746 | 0.9804 | |
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| 0.213 | 1.16 | 1000 | 0.0340 | 0.9948 | |
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| 0.159 | 1.28 | 1100 | 0.0426 | 0.9882 | |
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| 0.1048 | 1.39 | 1200 | 0.0248 | 0.9948 | |
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| 0.1493 | 1.51 | 1300 | 0.0154 | 0.9974 | |
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| 0.1274 | 1.63 | 1400 | 0.0394 | 0.9922 | |
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| 0.0915 | 1.74 | 1500 | 0.0422 | 0.9882 | |
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| 0.0598 | 1.86 | 1600 | 0.0219 | 0.9948 | |
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| 0.1241 | 1.97 | 1700 | 0.0173 | 0.9948 | |
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| 0.1249 | 2.09 | 1800 | 0.0179 | 0.9954 | |
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| 0.0131 | 2.21 | 1900 | 0.0124 | 0.9961 | |
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| 0.0392 | 2.32 | 2000 | 0.0123 | 0.9967 | |
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| 0.0655 | 2.44 | 2100 | 0.0223 | 0.9948 | |
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| 0.0355 | 2.56 | 2200 | 0.0256 | 0.9941 | |
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| 0.0335 | 2.67 | 2300 | 0.0147 | 0.9967 | |
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| 0.0618 | 2.79 | 2400 | 0.0123 | 0.9974 | |
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| 0.0476 | 2.9 | 2500 | 0.0110 | 0.9980 | |
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| 0.0452 | 3.02 | 2600 | 0.0192 | 0.9967 | |
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| 0.0104 | 3.14 | 2700 | 0.0184 | 0.9967 | |
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| 0.036 | 3.25 | 2800 | 0.0122 | 0.9974 | |
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| 0.0358 | 3.37 | 2900 | 0.0104 | 0.9987 | |
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| 0.054 | 3.48 | 3000 | 0.0101 | 0.9987 | |
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| 0.0395 | 3.6 | 3100 | 0.0132 | 0.9967 | |
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| 0.0367 | 3.72 | 3200 | 0.0096 | 0.9987 | |
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| 0.0261 | 3.83 | 3300 | 0.0101 | 0.9980 | |
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| 0.0017 | 3.95 | 3400 | 0.0096 | 0.9987 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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