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update model card README.md
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README.md
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---
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license: apache-2.0
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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: plant-seedlings-model-beit
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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: 0.8968565815324165
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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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# plant-seedlings-model-beit
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3466
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- Accuracy: 0.8969
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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: 12
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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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| 1.4966 | 0.8 | 100 | 1.2583 | 0.5909 |
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| 0.8239 | 1.6 | 200 | 0.9266 | 0.6979 |
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| 0.7583 | 2.4 | 300 | 0.6527 | 0.7834 |
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| 0.5222 | 3.2 | 400 | 0.5186 | 0.8035 |
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| 0.5233 | 4.0 | 500 | 0.5527 | 0.8060 |
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| 0.516 | 4.8 | 600 | 0.5558 | 0.8148 |
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| 0.4848 | 5.6 | 700 | 0.4780 | 0.8409 |
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| 0.1949 | 6.4 | 800 | 0.5876 | 0.8320 |
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| 0.2581 | 7.2 | 900 | 0.4364 | 0.8482 |
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| 0.2748 | 8.0 | 1000 | 0.3565 | 0.8777 |
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| 0.2973 | 8.8 | 1100 | 0.4623 | 0.8615 |
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| 0.1655 | 9.6 | 1200 | 0.3700 | 0.8875 |
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| 0.1744 | 10.4 | 1300 | 0.3751 | 0.8905 |
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| 0.3044 | 11.2 | 1400 | 0.3799 | 0.8919 |
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| 0.0981 | 12.0 | 1500 | 0.3466 | 0.8969 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 2.0.0+cu118
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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