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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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- 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[:20200] |
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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: 0.8853960396039604 |
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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: 0.4703 |
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- Accuracy: 0.8854 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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| 1.4019 | 1.0 | 1010 | 1.3796 | 0.8156 | |
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| 0.6238 | 2.0 | 2020 | 0.6604 | 0.8448 | |
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| 0.3691 | 3.0 | 3030 | 0.5661 | 0.8522 | |
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| 0.3947 | 4.0 | 4040 | 0.5226 | 0.8614 | |
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| 0.3511 | 5.0 | 5050 | 0.5125 | 0.8644 | |
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| 0.2504 | 6.0 | 6060 | 0.5180 | 0.8656 | |
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| 0.1285 | 7.0 | 7070 | 0.5312 | 0.8668 | |
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| 0.2301 | 8.0 | 8080 | 0.4779 | 0.875 | |
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| 0.0844 | 9.0 | 9090 | 0.4823 | 0.8839 | |
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| 0.1189 | 10.0 | 10100 | 0.4703 | 0.8854 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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