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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: isl-model
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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: 0.7533380182712579
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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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# isl-model
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9812
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- Accuracy: 0.7533
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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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| 2.6205 | 1.0 | 89 | 2.4275 | 0.5938 |
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| 1.6582 | 2.0 | 178 | 1.6078 | 0.7063 |
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| 1.3648 | 3.0 | 267 | 1.3754 | 0.7168 |
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| 1.1069 | 4.0 | 356 | 1.2056 | 0.7323 |
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| 1.1562 | 5.0 | 445 | 1.0958 | 0.7491 |
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| 1.1048 | 6.0 | 534 | 1.0221 | 0.7583 |
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| 0.9705 | 7.0 | 623 | 0.9831 | 0.7625 |
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| 0.9059 | 8.0 | 712 | 1.0279 | 0.7386 |
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| 0.9426 | 9.0 | 801 | 0.9511 | 0.7632 |
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| 0.8951 | 10.0 | 890 | 0.9812 | 0.7533 |
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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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