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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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: segformer-finetuned-food101 |
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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: 0.888 |
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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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# segformer-finetuned-food101 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3478 |
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- Accuracy: 0.888 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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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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| 2.0272 | 0.98 | 23 | 1.8039 | 0.329 | |
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| 1.5806 | 2.0 | 47 | 1.2465 | 0.608 | |
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| 1.0564 | 2.98 | 70 | 0.7507 | 0.756 | |
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| 0.7358 | 4.0 | 94 | 0.6263 | 0.784 | |
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| 0.6482 | 4.98 | 117 | 0.5551 | 0.795 | |
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| 0.5692 | 6.0 | 141 | 0.5849 | 0.794 | |
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| 0.5552 | 6.98 | 164 | 0.4931 | 0.831 | |
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| 0.4956 | 8.0 | 188 | 0.5166 | 0.83 | |
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| 0.4748 | 8.98 | 211 | 0.4808 | 0.834 | |
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| 0.424 | 10.0 | 235 | 0.4238 | 0.852 | |
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| 0.4314 | 10.98 | 258 | 0.4858 | 0.838 | |
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| 0.4071 | 12.0 | 282 | 0.4304 | 0.858 | |
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| 0.3928 | 12.98 | 305 | 0.4621 | 0.851 | |
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| 0.3695 | 14.0 | 329 | 0.4398 | 0.859 | |
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| 0.3704 | 14.98 | 352 | 0.4172 | 0.855 | |
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| 0.3299 | 16.0 | 376 | 0.4225 | 0.856 | |
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| 0.3391 | 16.98 | 399 | 0.4165 | 0.855 | |
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| 0.3023 | 18.0 | 423 | 0.3828 | 0.869 | |
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| 0.3318 | 18.98 | 446 | 0.4190 | 0.861 | |
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| 0.2994 | 20.0 | 470 | 0.4190 | 0.861 | |
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| 0.323 | 20.98 | 493 | 0.4034 | 0.866 | |
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| 0.2883 | 22.0 | 517 | 0.4083 | 0.874 | |
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| 0.2959 | 22.98 | 540 | 0.4202 | 0.862 | |
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| 0.2665 | 24.0 | 564 | 0.3740 | 0.881 | |
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| 0.2765 | 24.98 | 587 | 0.4123 | 0.866 | |
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| 0.2728 | 26.0 | 611 | 0.3763 | 0.868 | |
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| 0.2817 | 26.98 | 634 | 0.3939 | 0.864 | |
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| 0.2467 | 28.0 | 658 | 0.3938 | 0.87 | |
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| 0.2772 | 28.98 | 681 | 0.4013 | 0.866 | |
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| 0.2243 | 29.36 | 690 | 0.3478 | 0.888 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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