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--- |
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license: apache-2.0 |
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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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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: leaves |
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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: defect |
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type: imagefolder |
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config: Dhika--Leaves |
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split: validation |
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args: Dhika--Leaves |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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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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# leaves |
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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 defect dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0012 |
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- Accuracy: 1.0 |
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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: 10 |
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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: 50 |
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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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| 0.2249 | 1.25 | 10 | 0.0323 | 1.0 | |
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| 0.0177 | 2.5 | 20 | 0.0112 | 1.0 | |
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| 0.0086 | 3.75 | 30 | 0.0075 | 1.0 | |
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| 0.0063 | 5.0 | 40 | 0.0059 | 1.0 | |
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| 0.0051 | 6.25 | 50 | 0.0050 | 1.0 | |
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| 0.0045 | 7.5 | 60 | 0.0044 | 1.0 | |
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| 0.004 | 8.75 | 70 | 0.0040 | 1.0 | |
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| 0.0036 | 10.0 | 80 | 0.0036 | 1.0 | |
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| 0.0033 | 11.25 | 90 | 0.0034 | 1.0 | |
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| 0.0031 | 12.5 | 100 | 0.0031 | 1.0 | |
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| 0.0028 | 13.75 | 110 | 0.0029 | 1.0 | |
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| 0.0026 | 15.0 | 120 | 0.0027 | 1.0 | |
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| 0.0025 | 16.25 | 130 | 0.0025 | 1.0 | |
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| 0.0023 | 17.5 | 140 | 0.0024 | 1.0 | |
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| 0.0022 | 18.75 | 150 | 0.0023 | 1.0 | |
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| 0.0021 | 20.0 | 160 | 0.0021 | 1.0 | |
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| 0.002 | 21.25 | 170 | 0.0020 | 1.0 | |
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| 0.0019 | 22.5 | 180 | 0.0019 | 1.0 | |
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| 0.0018 | 23.75 | 190 | 0.0019 | 1.0 | |
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| 0.0017 | 25.0 | 200 | 0.0018 | 1.0 | |
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| 0.0016 | 26.25 | 210 | 0.0017 | 1.0 | |
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| 0.0016 | 27.5 | 220 | 0.0017 | 1.0 | |
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| 0.0015 | 28.75 | 230 | 0.0016 | 1.0 | |
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| 0.0015 | 30.0 | 240 | 0.0015 | 1.0 | |
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| 0.0014 | 31.25 | 250 | 0.0015 | 1.0 | |
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| 0.0014 | 32.5 | 260 | 0.0015 | 1.0 | |
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| 0.0013 | 33.75 | 270 | 0.0014 | 1.0 | |
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| 0.0013 | 35.0 | 280 | 0.0014 | 1.0 | |
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| 0.0013 | 36.25 | 290 | 0.0014 | 1.0 | |
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| 0.0013 | 37.5 | 300 | 0.0013 | 1.0 | |
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| 0.0012 | 38.75 | 310 | 0.0013 | 1.0 | |
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| 0.0012 | 40.0 | 320 | 0.0013 | 1.0 | |
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| 0.0012 | 41.25 | 330 | 0.0013 | 1.0 | |
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| 0.0012 | 42.5 | 340 | 0.0013 | 1.0 | |
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| 0.0012 | 43.75 | 350 | 0.0012 | 1.0 | |
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| 0.0012 | 45.0 | 360 | 0.0012 | 1.0 | |
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| 0.0011 | 46.25 | 370 | 0.0012 | 1.0 | |
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| 0.0012 | 47.5 | 380 | 0.0012 | 1.0 | |
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| 0.0011 | 48.75 | 390 | 0.0012 | 1.0 | |
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| 0.0011 | 50.0 | 400 | 0.0012 | 1.0 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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