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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: finetuned-SwinT-Indian-Food-Classification-v2 |
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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: Indian-Food-Images |
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type: imagefolder |
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config: default |
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split: train |
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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: 0.9458023379383634 |
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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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# finetuned-SwinT-Indian-Food-Classification-v2 |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the Indian-Food-Images dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2226 |
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- Accuracy: 0.9458 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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 |
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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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| 0.9351 | 0.3 | 100 | 0.6017 | 0.8363 | |
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| 0.5667 | 0.6 | 200 | 0.4384 | 0.8767 | |
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| 0.5548 | 0.9 | 300 | 0.4215 | 0.8767 | |
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| 0.5516 | 1.2 | 400 | 0.4290 | 0.8735 | |
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| 0.3782 | 1.5 | 500 | 0.3502 | 0.8980 | |
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| 0.3115 | 1.8 | 600 | 0.3780 | 0.8937 | |
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| 0.4229 | 2.1 | 700 | 0.3545 | 0.8905 | |
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| 0.3832 | 2.4 | 800 | 0.3446 | 0.9086 | |
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| 0.2745 | 2.7 | 900 | 0.3299 | 0.9150 | |
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| 0.2063 | 3.0 | 1000 | 0.2592 | 0.9277 | |
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| 0.2077 | 3.3 | 1100 | 0.3772 | 0.9150 | |
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| 0.2041 | 3.6 | 1200 | 0.2855 | 0.9214 | |
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| 0.2541 | 3.9 | 1300 | 0.2502 | 0.9330 | |
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| 0.1203 | 4.2 | 1400 | 0.2577 | 0.9362 | |
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| 0.1594 | 4.5 | 1500 | 0.2226 | 0.9458 | |
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| 0.1015 | 4.8 | 1600 | 0.2368 | 0.9437 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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