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Jacques7103
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Food-Recognition

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
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  1. README.md +16 -6
  2. model.safetensors +1 -1
  3. runs/Dec04_21-54-12_LAPTOP-PH299HA6/events.out.tfevents.1701701663.LAPTOP-PH299HA6.22060.0 +3 -0
  4. runs/Dec04_22-01-11_LAPTOP-PH299HA6/events.out.tfevents.1701702084.LAPTOP-PH299HA6.22060.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -2,7 +2,6 @@
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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
@@ -16,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # food-recognition
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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: 4.0433
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- - Accuracy: 0.4436
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  ## Model description
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@@ -50,8 +49,19 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.1598 | 1.45 | 100 | 4.0433 | 0.4436 |
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- | 0.038 | 2.9 | 200 | 4.6230 | 0.4518 |
 
 
 
 
 
 
 
 
 
 
 
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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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  # food-recognition
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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: 6.4434
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+ - Accuracy: 0.346
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.8835 | 0.29 | 100 | 4.2128 | 0.3025 |
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+ | 0.4912 | 0.58 | 200 | 4.7446 | 0.3235 |
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+ | 0.4643 | 0.87 | 300 | 5.0883 | 0.3189 |
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+ | 0.247 | 1.16 | 400 | 5.4459 | 0.3329 |
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+ | 0.2823 | 1.45 | 500 | 5.7501 | 0.3342 |
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+ | 0.2834 | 1.74 | 600 | 5.8456 | 0.3333 |
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+ | 0.0732 | 2.03 | 700 | 5.8882 | 0.336 |
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+ | 0.0235 | 2.33 | 800 | 6.1063 | 0.34 |
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+ | 0.046 | 2.62 | 900 | 6.2081 | 0.3438 |
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+ | 0.0393 | 2.91 | 1000 | 6.3318 | 0.3411 |
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+ | 0.009 | 3.2 | 1100 | 6.3580 | 0.3456 |
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+ | 0.0285 | 3.49 | 1200 | 6.4274 | 0.3435 |
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+ | 0.0095 | 3.78 | 1300 | 6.4434 | 0.346 |
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  ### Framework versions
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