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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-p16-384-1e-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: 0.9798850574712644 |
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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-p16-384-1e-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: 0.0907 |
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- Accuracy: 0.9799 |
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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: 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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- 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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| 0.2663 | 0.07 | 100 | 0.2025 | 0.9368 | |
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| 0.1384 | 0.14 | 200 | 0.2169 | 0.9382 | |
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| 0.0582 | 0.21 | 300 | 0.0932 | 0.9641 | |
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| 0.1129 | 0.28 | 400 | 0.1382 | 0.9555 | |
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| 0.0575 | 0.35 | 500 | 0.1204 | 0.9684 | |
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| 0.1027 | 0.42 | 600 | 0.0923 | 0.9684 | |
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| 0.0369 | 0.49 | 700 | 0.1114 | 0.9655 | |
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| 0.015 | 0.56 | 800 | 0.1745 | 0.9540 | |
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| 0.0455 | 0.63 | 900 | 0.0871 | 0.9655 | |
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| 0.0129 | 0.7 | 1000 | 0.1222 | 0.9626 | |
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| 0.0623 | 0.77 | 1100 | 0.0981 | 0.9670 | |
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| 0.0328 | 0.84 | 1200 | 0.0956 | 0.9655 | |
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| 0.0515 | 0.91 | 1300 | 0.0740 | 0.9756 | |
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| 0.0513 | 0.97 | 1400 | 0.0696 | 0.9756 | |
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| 0.0005 | 1.04 | 1500 | 0.0757 | 0.9784 | |
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| 0.0606 | 1.11 | 1600 | 0.0869 | 0.9784 | |
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| 0.0507 | 1.18 | 1700 | 0.1121 | 0.9698 | |
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| 0.0004 | 1.25 | 1800 | 0.0562 | 0.9813 | |
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| 0.0013 | 1.32 | 1900 | 0.0455 | 0.9828 | |
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| 0.0309 | 1.39 | 2000 | 0.0752 | 0.9799 | |
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| 0.0096 | 1.46 | 2100 | 0.0739 | 0.9770 | |
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| 0.001 | 1.53 | 2200 | 0.0536 | 0.9842 | |
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| 0.0892 | 1.6 | 2300 | 0.0728 | 0.9799 | |
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| 0.0568 | 1.67 | 2400 | 0.2331 | 0.9670 | |
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| 0.0049 | 1.74 | 2500 | 0.0924 | 0.9784 | |
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| 0.0003 | 1.81 | 2600 | 0.0922 | 0.9770 | |
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| 0.0004 | 1.88 | 2700 | 0.1383 | 0.9756 | |
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| 0.0001 | 1.95 | 2800 | 0.1568 | 0.9670 | |
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| 0.0001 | 2.02 | 2900 | 0.1299 | 0.9741 | |
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| 0.0002 | 2.09 | 3000 | 0.0976 | 0.9828 | |
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| 0.0 | 2.16 | 3100 | 0.0536 | 0.9885 | |
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| 0.004 | 2.23 | 3200 | 0.1074 | 0.9770 | |
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| 0.0001 | 2.3 | 3300 | 0.0702 | 0.9828 | |
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| 0.008 | 2.37 | 3400 | 0.1185 | 0.9756 | |
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| 0.0212 | 2.44 | 3500 | 0.0793 | 0.9756 | |
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| 0.0001 | 2.51 | 3600 | 0.1402 | 0.9698 | |
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| 0.0001 | 2.58 | 3700 | 0.0761 | 0.9828 | |
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| 0.0134 | 2.65 | 3800 | 0.1132 | 0.9741 | |
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| 0.0001 | 2.72 | 3900 | 0.0703 | 0.9828 | |
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| 0.0 | 2.79 | 4000 | 0.0764 | 0.9799 | |
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| 0.0 | 2.86 | 4100 | 0.0737 | 0.9828 | |
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| 0.0011 | 2.92 | 4200 | 0.1525 | 0.9727 | |
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| 0.019 | 2.99 | 4300 | 0.1078 | 0.9799 | |
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| 0.0 | 3.06 | 4400 | 0.0774 | 0.9828 | |
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| 0.0 | 3.13 | 4500 | 0.1081 | 0.9799 | |
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| 0.0 | 3.2 | 4600 | 0.0995 | 0.9828 | |
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| 0.0 | 3.27 | 4700 | 0.0861 | 0.9856 | |
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| 0.0 | 3.34 | 4800 | 0.0852 | 0.9856 | |
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| 0.0 | 3.41 | 4900 | 0.0834 | 0.9856 | |
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| 0.0 | 3.48 | 5000 | 0.0932 | 0.9828 | |
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| 0.0 | 3.55 | 5100 | 0.0837 | 0.9828 | |
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| 0.0 | 3.62 | 5200 | 0.0854 | 0.9813 | |
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| 0.0 | 3.69 | 5300 | 0.0850 | 0.9813 | |
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| 0.0 | 3.76 | 5400 | 0.0842 | 0.9813 | |
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| 0.0 | 3.83 | 5500 | 0.0911 | 0.9813 | |
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| 0.0 | 3.9 | 5600 | 0.0913 | 0.9813 | |
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| 0.0 | 3.97 | 5700 | 0.0907 | 0.9799 | |
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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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