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update model card README.md
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README.md
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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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- 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: imagefolder
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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: 0.9436769394261424
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2368
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- Accuracy: 0.9437
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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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