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--- |
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base_model: google/vit-base-patch16-224-in21k |
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library_name: peft |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: vit-base-patch16-224-in21k-finetuned-tekno23 |
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results: [] |
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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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# vit-base-patch16-224-in21k-finetuned-tekno23 |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2583 |
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- Accuracy: 0.4014 |
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- F1: 0.3092 |
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- Precision: 0.3870 |
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- Recall: 0.4014 |
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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: 5 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.3662 | 1.0 | 421 | 1.3619 | 0.3190 | 0.1618 | 0.2852 | 0.3190 | |
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| 1.3095 | 2.0 | 842 | 1.2970 | 0.3962 | 0.2933 | 0.4359 | 0.3962 | |
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| 1.2683 | 3.0 | 1263 | 1.2687 | 0.4020 | 0.3081 | 0.3550 | 0.4020 | |
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| 1.2524 | 4.0 | 1684 | 1.2576 | 0.4057 | 0.3098 | 0.3951 | 0.4057 | |
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| 1.2781 | 5.0 | 2105 | 1.2583 | 0.4014 | 0.3092 | 0.3870 | 0.4014 | |
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### Framework versions |
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- PEFT 0.12.1.dev0 |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
