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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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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: my_awesome_food_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: food101 |
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type: food101 |
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config: default |
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split: train[:5000] |
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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: 0.9 |
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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_awesome_food_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 food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8834 |
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- Accuracy: 0.9 |
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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: 3e-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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| 3.6073 | 0.99 | 62 | 3.3725 | 0.818 | |
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| 2.2956 | 2.0 | 125 | 2.1579 | 0.854 | |
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| 1.7042 | 2.99 | 187 | 1.6201 | 0.887 | |
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| 1.3278 | 4.0 | 250 | 1.3513 | 0.89 | |
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| 1.1314 | 4.99 | 312 | 1.1549 | 0.908 | |
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| 1.007 | 6.0 | 375 | 1.0737 | 0.889 | |
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| 0.905 | 6.99 | 437 | 0.9600 | 0.906 | |
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| 0.8227 | 8.0 | 500 | 0.9113 | 0.912 | |
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| 0.7948 | 8.99 | 562 | 0.8908 | 0.909 | |
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| 0.7598 | 9.92 | 620 | 0.8834 | 0.9 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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