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rohan4s
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finetuned-bangladeshi-traditional-food

Image Classification
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vit
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  1. README.md +7 -8
  2. model.safetensors +1 -1
  3. runs/Dec09_10-03-07_09ff2b7e7cec/events.out.tfevents.1733738662.09ff2b7e7cec.23.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8713
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- - Accuracy: 0.9050
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  ## Model description
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@@ -44,18 +44,17 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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- | 0.2055 | 1.0 | 64 | 1.0229 | 0.8436 |
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- | 0.1685 | 2.0 | 128 | 0.8520 | 0.8855 |
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- | 0.0168 | 3.0 | 192 | 0.8259 | 0.8994 |
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- | 0.054 | 4.0 | 256 | 0.8865 | 0.8911 |
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- | 0.0236 | 5.0 | 320 | 0.8713 | 0.9050 |
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  ### Framework versions
 
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4262
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+ - Accuracy: 0.9335
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 4
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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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+ | 1.1167 | 1.0 | 51 | 1.0463 | 0.8473 |
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+ | 0.5904 | 2.0 | 102 | 0.6225 | 0.8941 |
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+ | 0.4023 | 3.0 | 153 | 0.5122 | 0.8966 |
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+ | 0.2824 | 4.0 | 204 | 0.4262 | 0.9335 |
 
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  ### Framework versions
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