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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-tiny-patch4-window8-256 |
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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: swinv2-tiny-patch4-window8-256-finetuned-eurosat |
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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: 0.8858613861386139 |
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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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# swinv2-tiny-patch4-window8-256-finetuned-eurosat |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3997 |
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- Accuracy: 0.8859 |
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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: 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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| 1.8552 | 1.0 | 592 | 1.1245 | 0.6955 | |
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| 1.2938 | 2.0 | 1184 | 0.6712 | 0.8131 | |
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| 1.2294 | 3.0 | 1776 | 0.5354 | 0.8492 | |
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| 1.0199 | 4.0 | 2368 | 0.4958 | 0.8594 | |
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| 0.9914 | 5.0 | 2960 | 0.4633 | 0.8678 | |
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| 0.8786 | 6.0 | 3552 | 0.4390 | 0.8750 | |
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| 0.806 | 7.0 | 4144 | 0.4206 | 0.8791 | |
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| 0.7506 | 8.0 | 4736 | 0.4093 | 0.8832 | |
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| 0.7433 | 9.0 | 5328 | 0.4053 | 0.8841 | |
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| 0.6393 | 10.0 | 5920 | 0.3997 | 0.8859 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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