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finetuned-fake-food

Image Classification
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vit
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  1. README.md +25 -47
  2. model.safetensors +1 -1
  3. runs/Oct01_11-46-07_a59caee1d103/events.out.tfevents.1727783176.a59caee1d103.4704.9 +3 -0
  4. runs/Oct01_11-46-38_a59caee1d103/events.out.tfevents.1727783205.a59caee1d103.4704.10 +3 -0
  5. runs/Oct01_11-51-38_a59caee1d103/events.out.tfevents.1727783507.a59caee1d103.4704.11 +3 -0
  6. runs/Oct01_12-13-49_a59caee1d103/events.out.tfevents.1727784840.a59caee1d103.4704.12 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -3,7 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -17,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # finetuned-fake-food
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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 the indian_food_images dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6574
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- - Accuracy: 0.6164
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  ## Model description
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@@ -40,59 +39,38 @@ More information needed
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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: 2
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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: 10
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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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- | 0.6977 | 0.2421 | 100 | 0.6805 | 0.5822 |
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- | 0.6956 | 0.4843 | 200 | 0.6936 | 0.4178 |
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- | 0.6795 | 0.7264 | 300 | 0.6734 | 0.6507 |
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- | 0.7061 | 0.9685 | 400 | 0.6760 | 0.5822 |
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- | 0.6941 | 1.2107 | 500 | 0.6746 | 0.5822 |
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- | 0.6898 | 1.4528 | 600 | 0.6675 | 0.6027 |
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- | 0.6956 | 1.6949 | 700 | 0.6846 | 0.5753 |
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- | 0.6847 | 1.9370 | 800 | 0.6746 | 0.5822 |
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- | 0.6949 | 2.1792 | 900 | 0.6780 | 0.5890 |
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- | 0.703 | 2.4213 | 1000 | 0.6895 | 0.5753 |
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- | 0.6851 | 2.6634 | 1100 | 0.6742 | 0.5822 |
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- | 0.6878 | 2.9056 | 1200 | 0.6742 | 0.6301 |
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- | 0.68 | 3.1477 | 1300 | 0.6713 | 0.5822 |
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- | 0.6728 | 3.3898 | 1400 | 0.6838 | 0.5959 |
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- | 0.698 | 3.6320 | 1500 | 0.6775 | 0.5822 |
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- | 0.7033 | 3.8741 | 1600 | 0.6735 | 0.5822 |
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- | 0.6973 | 4.1162 | 1700 | 0.6804 | 0.6233 |
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- | 0.6822 | 4.3584 | 1800 | 0.6848 | 0.6027 |
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- | 0.6896 | 4.6005 | 1900 | 0.6835 | 0.5411 |
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- | 0.6772 | 4.8426 | 2000 | 0.6753 | 0.6096 |
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- | 0.6843 | 5.0847 | 2100 | 0.6667 | 0.5890 |
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- | 0.6898 | 5.3269 | 2200 | 0.6726 | 0.5822 |
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- | 0.6868 | 5.5690 | 2300 | 0.6784 | 0.5616 |
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- | 0.6636 | 5.8111 | 2400 | 0.6640 | 0.6301 |
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- | 0.6833 | 6.0533 | 2500 | 0.6768 | 0.5137 |
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- | 0.678 | 6.2954 | 2600 | 0.6652 | 0.6233 |
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- | 0.6672 | 6.5375 | 2700 | 0.6735 | 0.5479 |
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- | 0.6975 | 6.7797 | 2800 | 0.6687 | 0.5890 |
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- | 0.6858 | 7.0218 | 2900 | 0.6672 | 0.6027 |
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- | 0.6687 | 7.2639 | 3000 | 0.6648 | 0.5753 |
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- | 0.6636 | 7.5061 | 3100 | 0.6674 | 0.5685 |
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- | 0.6904 | 7.7482 | 3200 | 0.6752 | 0.5342 |
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- | 0.6585 | 7.9903 | 3300 | 0.7023 | 0.5959 |
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- | 0.6874 | 8.2324 | 3400 | 0.6615 | 0.5753 |
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- | 0.6444 | 8.4746 | 3500 | 0.7721 | 0.5205 |
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- | 0.6803 | 8.7167 | 3600 | 0.6809 | 0.5822 |
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- | 0.6782 | 8.9588 | 3700 | 0.6638 | 0.5822 |
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- | 0.6536 | 9.2010 | 3800 | 0.6607 | 0.6233 |
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- | 0.6188 | 9.4431 | 3900 | 0.7090 | 0.5685 |
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- | 0.7026 | 9.6852 | 4000 | 0.6574 | 0.6164 |
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- | 0.7008 | 9.9274 | 4100 | 0.6577 | 0.6096 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  # finetuned-fake-food
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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: 0.3455
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+ - Accuracy: 0.8541
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  ## Model description
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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: 8
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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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+ - training_steps: 2000
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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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+ | 0.5416 | 0.1264 | 100 | 0.5593 | 0.7081 |
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+ | 0.5299 | 0.2528 | 200 | 0.5342 | 0.7422 |
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+ | 0.5503 | 0.3793 | 300 | 0.4875 | 0.7717 |
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+ | 0.5561 | 0.5057 | 400 | 0.4622 | 0.7941 |
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+ | 0.5581 | 0.6321 | 500 | 0.5501 | 0.7457 |
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+ | 0.5845 | 0.7585 | 600 | 0.5088 | 0.7475 |
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+ | 0.5695 | 0.8850 | 700 | 0.4740 | 0.7860 |
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+ | 0.5406 | 1.0114 | 800 | 0.4856 | 0.7816 |
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+ | 0.5353 | 1.1378 | 900 | 0.4252 | 0.8156 |
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+ | 0.5345 | 1.2642 | 1000 | 0.5014 | 0.7762 |
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+ | 0.5105 | 1.3906 | 1100 | 0.4800 | 0.7860 |
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+ | 0.5266 | 1.5171 | 1200 | 0.4618 | 0.7959 |
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+ | 0.4709 | 1.6435 | 1300 | 0.3906 | 0.8281 |
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+ | 0.4624 | 1.7699 | 1400 | 0.4208 | 0.8129 |
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+ | 0.4677 | 1.8963 | 1500 | 0.4207 | 0.8174 |
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+ | 0.4478 | 2.0228 | 1600 | 0.3557 | 0.8478 |
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+ | 0.4451 | 2.1492 | 1700 | 0.3546 | 0.8442 |
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+ | 0.3796 | 2.2756 | 1800 | 0.3199 | 0.8720 |
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+ | 0.4358 | 2.4020 | 1900 | 0.3308 | 0.8603 |
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+ | 0.3373 | 2.5284 | 2000 | 0.3455 | 0.8541 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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