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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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- image-classification |
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- generated_from_trainer |
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datasets: |
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- renovation |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-beans-demo-v5 |
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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: beans |
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type: renovation |
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config: default |
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split: validation |
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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: 0.6695059625212947 |
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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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# vit-base-beans-demo-v5 |
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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 the beans dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8460 |
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- Accuracy: 0.6695 |
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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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- 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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| 1.0616 | 0.17 | 100 | 1.0267 | 0.5818 | |
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| 0.9594 | 0.34 | 200 | 0.9468 | 0.6073 | |
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| 1.1785 | 0.51 | 300 | 0.9976 | 0.5869 | |
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| 0.865 | 0.68 | 400 | 0.9288 | 0.6388 | |
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| 0.8494 | 0.85 | 500 | 0.8573 | 0.6516 | |
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| 0.8151 | 1.02 | 600 | 0.8729 | 0.6397 | |
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| 0.5787 | 1.19 | 700 | 0.9067 | 0.6448 | |
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| 0.7768 | 1.36 | 800 | 0.8996 | 0.6533 | |
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| 0.6098 | 1.53 | 900 | 0.8460 | 0.6695 | |
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| 0.6251 | 1.7 | 1000 | 0.8610 | 0.6704 | |
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| 0.7863 | 1.87 | 1100 | 0.8668 | 0.6431 | |
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| 0.2595 | 2.04 | 1200 | 0.8725 | 0.6840 | |
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| 0.2735 | 2.21 | 1300 | 0.9307 | 0.6746 | |
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| 0.2429 | 2.39 | 1400 | 1.0958 | 0.6354 | |
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| 0.3224 | 2.56 | 1500 | 1.0305 | 0.6687 | |
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| 0.1602 | 2.73 | 1600 | 1.0072 | 0.6746 | |
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| 0.2042 | 2.9 | 1700 | 1.0971 | 0.6789 | |
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| 0.0604 | 3.07 | 1800 | 1.0817 | 0.6917 | |
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| 0.0716 | 3.24 | 1900 | 1.1307 | 0.6925 | |
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| 0.0822 | 3.41 | 2000 | 1.1827 | 0.6925 | |
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| 0.0889 | 3.58 | 2100 | 1.2424 | 0.6934 | |
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| 0.0855 | 3.75 | 2200 | 1.2667 | 0.6899 | |
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| 0.0682 | 3.92 | 2300 | 1.2470 | 0.6951 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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