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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-384 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.985632183908046 |
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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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# rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24 |
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This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0544 |
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- Accuracy: 0.9856 |
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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: 2 |
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- mixed_precision_training: Native AMP |
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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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| 3.0503 | 0.07 | 100 | 3.0273 | 0.0934 | |
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| 2.2522 | 0.14 | 200 | 2.4833 | 0.2328 | |
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| 1.5093 | 0.21 | 300 | 1.3361 | 0.5503 | |
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| 1.0645 | 0.28 | 400 | 1.0976 | 0.6580 | |
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| 0.5308 | 0.35 | 500 | 0.5680 | 0.8161 | |
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| 0.3545 | 0.42 | 600 | 0.3870 | 0.8664 | |
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| 0.2051 | 0.49 | 700 | 0.3348 | 0.9023 | |
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| 0.2241 | 0.56 | 800 | 0.1545 | 0.9411 | |
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| 0.2165 | 0.63 | 900 | 0.1722 | 0.9569 | |
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| 0.1589 | 0.7 | 1000 | 0.1554 | 0.9497 | |
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| 0.0647 | 0.77 | 1100 | 0.1400 | 0.9483 | |
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| 0.1178 | 0.84 | 1200 | 0.2000 | 0.9411 | |
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| 0.0518 | 0.91 | 1300 | 0.1856 | 0.9483 | |
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| 0.0433 | 0.97 | 1400 | 0.1573 | 0.9468 | |
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| 0.0228 | 1.04 | 1500 | 0.1156 | 0.9626 | |
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| 0.1261 | 1.11 | 1600 | 0.0628 | 0.9727 | |
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| 0.001 | 1.18 | 1700 | 0.0730 | 0.9770 | |
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| 0.0515 | 1.25 | 1800 | 0.1589 | 0.9468 | |
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| 0.0195 | 1.32 | 1900 | 0.1114 | 0.9641 | |
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| 0.0696 | 1.39 | 2000 | 0.1507 | 0.9555 | |
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| 0.0006 | 1.46 | 2100 | 0.0799 | 0.9741 | |
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| 0.0063 | 1.53 | 2200 | 0.0979 | 0.9684 | |
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| 0.0337 | 1.6 | 2300 | 0.1191 | 0.9598 | |
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| 0.0261 | 1.67 | 2400 | 0.0839 | 0.9727 | |
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| 0.001 | 1.74 | 2500 | 0.0911 | 0.9770 | |
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| 0.001 | 1.81 | 2600 | 0.0726 | 0.9799 | |
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| 0.0003 | 1.88 | 2700 | 0.0581 | 0.9842 | |
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| 0.0004 | 1.95 | 2800 | 0.0544 | 0.9856 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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