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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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- image-classification |
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- pytorch |
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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: food101_outputs |
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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: food-101 |
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type: food101 |
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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: 0.8912871287128713 |
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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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# nateraw/food |
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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 nateraw/food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4501 |
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- Accuracy: 0.8913 |
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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: 0.0002 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 1337 |
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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: 5.0 |
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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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| 0.8271 | 1.0 | 592 | 0.6070 | 0.8562 | |
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| 0.4376 | 2.0 | 1184 | 0.4947 | 0.8691 | |
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| 0.2089 | 3.0 | 1776 | 0.4876 | 0.8747 | |
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| 0.0882 | 4.0 | 2368 | 0.4639 | 0.8857 | |
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| 0.0452 | 5.0 | 2960 | 0.4501 | 0.8913 | |
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### Framework versions |
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- Transformers 4.9.0.dev0 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.9.1.dev0 |
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- Tokenizers 0.10.3 |
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