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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: my_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: 0.7283950617283951
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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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# my_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: 1.9092
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- Accuracy: 0.7284
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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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| 3.1292 | 0.96 | 11 | 3.0256 | 0.2593 |
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| 2.9426 | 2.0 | 23 | 2.7796 | 0.2716 |
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| 2.706 | 2.96 | 34 | 2.5462 | 0.4321 |
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| 2.5389 | 4.0 | 46 | 2.4454 | 0.4568 |
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| 2.3638 | 4.96 | 57 | 2.2169 | 0.6914 |
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| 2.1862 | 6.0 | 69 | 2.1349 | 0.6296 |
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| 2.0459 | 6.96 | 80 | 2.1135 | 0.6049 |
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| 1.9912 | 8.0 | 92 | 1.9757 | 0.7531 |
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| 1.9504 | 8.96 | 103 | 1.9073 | 0.7407 |
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| 1.942 | 9.57 | 110 | 1.9092 | 0.7284 |
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### Framework versions
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- Transformers 4.27.4
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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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