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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: results |
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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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# results |
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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: 0.1114 |
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- Accuracy: 0.9687 |
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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: 3e-05 |
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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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- gradient_accumulation_steps: 8 |
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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: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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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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| 0.6639 | 0.1829 | 100 | 0.6155 | 0.6554 | |
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| 0.4191 | 0.3657 | 200 | 0.3088 | 0.8959 | |
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| 0.1698 | 0.5486 | 300 | 0.5321 | 0.7281 | |
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| 0.0749 | 0.7314 | 400 | 0.5087 | 0.7900 | |
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| 0.0484 | 0.9143 | 500 | 0.4649 | 0.8185 | |
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| 0.0323 | 1.0971 | 600 | 0.6888 | 0.762 | |
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| 0.0264 | 1.28 | 700 | 0.1395 | 0.9513 | |
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| 0.0224 | 1.4629 | 800 | 0.0661 | 0.9776 | |
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| 0.02 | 1.6457 | 900 | 0.1173 | 0.9581 | |
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| 0.0168 | 1.8286 | 1000 | 0.3498 | 0.889 | |
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| 0.013 | 2.0114 | 1100 | 0.1053 | 0.9655 | |
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| 0.0087 | 2.1943 | 1200 | 0.3601 | 0.8947 | |
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| 0.0081 | 2.3771 | 1300 | 0.1508 | 0.9535 | |
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| 0.0073 | 2.56 | 1400 | 0.2090 | 0.9390 | |
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| 0.0056 | 2.7429 | 1500 | 0.1136 | 0.9649 | |
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| 0.005 | 2.9257 | 1600 | 0.2656 | 0.9206 | |
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| 0.0036 | 3.1086 | 1700 | 0.1320 | 0.9595 | |
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| 0.002 | 3.2914 | 1800 | 0.1068 | 0.9686 | |
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| 0.0018 | 3.4743 | 1900 | 0.1091 | 0.9690 | |
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| 0.0019 | 3.6571 | 2000 | 0.1114 | 0.9687 | |
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| 0.0018 | 3.84 | 2100 | 0.0968 | 0.9719 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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