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--- |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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datasets: |
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- food101 |
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metrics: |
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- accuracy |
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model-index: |
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- name: lr6e-05 |
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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: food101 |
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type: food101 |
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config: default |
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split: validation |
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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: 0.8971089108910891 |
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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-food101 |
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This model is a fine-tuned version of [eslamxm/vit-base-food101](https://huggingface.co/eslamxm/vit-base-food101) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3856 |
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- Accuracy: 0.8971 |
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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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## Script |
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```python |
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"cmd_list": [ |
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"python", |
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"run_image_classification.py", |
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"--model_name_or_path", |
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"eslamxm/vit-base-food101", |
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"--dataset_name", |
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"food101", |
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"--output_dir", |
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"<output_dir>", |
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"--overwrite_output_dir", |
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"--remove_unused_columns", |
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"False", |
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"--do_train", |
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"--do_eval", |
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"--optim", |
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"adamw_torch", |
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"--learning_rate", |
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"6e-05", |
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"--num_train_epochs", |
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"3", |
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"--dataloader_num_workers", |
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"10", |
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"--per_device_train_batch_size", |
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"64", |
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"--gradient_accumulation_steps", |
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"2", |
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"--per_device_eval_batch_size", |
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"128", |
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"--logging_strategy", |
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"steps", |
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"--logging_steps", |
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"10", |
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"--evaluation_strategy", |
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"steps", |
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"--eval_steps", |
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"500", |
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"--save_steps", |
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"500", |
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"--evaluation_strategy", |
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"epoch", |
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"--save_strategy", |
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"epoch", |
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"--load_best_model_at_end", |
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"False", |
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"--save_total_limit", |
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"1", |
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"--seed", |
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"42", |
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"--fp16" |
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] |
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``` |
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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: 6e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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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: 3.0 |
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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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| 0.3687 | 1.0 | 592 | 0.4044 | 0.8889 | |
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| 0.3422 | 2.0 | 1184 | 0.3911 | 0.8953 | |
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| 0.3808 | 3.0 | 1776 | 0.3856 | 0.8971 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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