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: weeds_swin_balanced
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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: test
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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: 0.9666666666666667
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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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# weeds_swin_balanced
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1356
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- Accuracy: 0.9667
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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: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10
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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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| 0.5298 | 1.0 | 150 | 0.3729 | 0.8633 |
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| 0.4204 | 2.0 | 300 | 0.2560 | 0.8933 |
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| 0.3229 | 3.0 | 450 | 0.3213 | 0.88 |
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| 0.2347 | 4.0 | 600 | 0.2031 | 0.9233 |
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| 0.3342 | 5.0 | 750 | 0.1912 | 0.9367 |
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| 0.2106 | 6.0 | 900 | 0.1365 | 0.9467 |
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| 0.1891 | 7.0 | 1050 | 0.1927 | 0.97 |
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| 0.0629 | 8.0 | 1200 | 0.1726 | 0.9467 |
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| 0.1169 | 9.0 | 1350 | 0.1363 | 0.9633 |
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| 0.0426 | 10.0 | 1500 | 0.1356 | 0.9667 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu117
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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