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NabeelShar
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emotions_classifier

Image Classification
Transformers
TensorFlow
vit
generated_from_keras_callback
Inference Endpoints
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NabeelShar commited on
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End of training

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  1. README.md +55 -10
  2. tf_model.h5 +1 -1
README.md CHANGED
@@ -15,10 +15,10 @@ 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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- - Train Loss: 1.7648
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- - Validation Loss: 1.8114
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- - Train Accuracy: 0.1625
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- - Epoch: 4
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 3200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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  - training_precision: float32
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  ### Training results
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  | Train Loss | Validation Loss | Train Accuracy | Epoch |
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  |:----------:|:---------------:|:--------------:|:-----:|
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- | 2.0762 | 1.9409 | 0.225 | 0 |
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- | 2.0248 | 2.0010 | 0.1562 | 1 |
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- | 1.9520 | 2.0716 | 0.1437 | 2 |
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- | 1.8909 | 1.7114 | 0.2313 | 3 |
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- | 1.7648 | 1.8114 | 0.1625 | 4 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ - Train Loss: 1.1146
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+ - Validation Loss: 1.6637
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+ - Train Accuracy: 0.3625
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+ - Epoch: 49
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 32000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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  - training_precision: float32
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  ### Training results
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  | Train Loss | Validation Loss | Train Accuracy | Epoch |
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  |:----------:|:---------------:|:--------------:|:-----:|
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+ | 2.0910 | 2.0947 | 0.1062 | 0 |
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+ | 2.0049 | 1.9103 | 0.2062 | 1 |
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+ | 2.0473 | 1.9654 | 0.175 | 2 |
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+ | 1.9824 | 2.1773 | 0.125 | 3 |
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+ | 2.0538 | 2.0144 | 0.1875 | 4 |
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+ | 1.9921 | 2.0826 | 0.1437 | 5 |
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+ | 2.0904 | 2.0995 | 0.1812 | 6 |
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+ | 2.0866 | 2.0908 | 0.1313 | 7 |
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+ | 2.0718 | 2.0800 | 0.125 | 8 |
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+ | 2.0511 | 2.0358 | 0.1938 | 9 |
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+ | 1.9794 | 1.9049 | 0.2313 | 10 |
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+ | 1.9289 | 1.8717 | 0.1875 | 11 |
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+ | 1.8696 | 1.8451 | 0.2062 | 12 |
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+ | 1.8361 | 1.8010 | 0.2062 | 13 |
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+ | 1.8122 | 1.7457 | 0.225 | 14 |
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+ | 1.7571 | 1.7331 | 0.25 | 15 |
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+ | 1.6846 | 1.8783 | 0.25 | 16 |
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+ | 1.6954 | 1.8015 | 0.25 | 17 |
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+ | 1.7414 | 1.7329 | 0.1625 | 18 |
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+ | 1.6662 | 1.6900 | 0.2625 | 19 |
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+ | 1.6322 | 1.7607 | 0.25 | 20 |
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+ | 1.5822 | 1.6670 | 0.3063 | 21 |
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+ | 1.6279 | 1.6800 | 0.3 | 22 |
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+ | 1.5737 | 1.7843 | 0.25 | 23 |
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+ | 1.5851 | 1.6927 | 0.2875 | 24 |
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+ | 1.4926 | 1.6640 | 0.2687 | 25 |
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+ | 1.4879 | 1.7408 | 0.2812 | 26 |
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+ | 1.5564 | 1.6668 | 0.275 | 27 |
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+ | 1.5093 | 1.6259 | 0.3187 | 28 |
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+ | 1.4428 | 1.6973 | 0.2437 | 29 |
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+ | 1.4328 | 1.6412 | 0.2812 | 30 |
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+ | 1.3778 | 1.6470 | 0.3187 | 31 |
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+ | 1.4635 | 1.6411 | 0.325 | 32 |
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+ | 1.4044 | 1.6643 | 0.2938 | 33 |
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+ | 1.2991 | 1.6864 | 0.2875 | 34 |
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+ | 1.3467 | 1.6124 | 0.2687 | 35 |
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+ | 1.3422 | 1.6517 | 0.2687 | 36 |
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+ | 1.3998 | 1.5634 | 0.325 | 37 |
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+ | 1.2963 | 1.7403 | 0.3 | 38 |
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+ | 1.3050 | 1.7550 | 0.3187 | 39 |
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+ | 1.2988 | 1.6917 | 0.3438 | 40 |
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+ | 1.2601 | 1.6739 | 0.3125 | 41 |
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+ | 1.1943 | 1.7200 | 0.35 | 42 |
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+ | 1.2663 | 1.6505 | 0.3312 | 43 |
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+ | 1.2228 | 1.7337 | 0.3312 | 44 |
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+ | 1.1413 | 1.7777 | 0.2812 | 45 |
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+ | 1.1429 | 1.7138 | 0.3375 | 46 |
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+ | 1.0760 | 1.7160 | 0.3187 | 47 |
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+ | 1.1625 | 1.8049 | 0.3063 | 48 |
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+ | 1.1146 | 1.6637 | 0.3625 | 49 |
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  ### Framework versions
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