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kensvin
/
emotion_classification

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
kensvin commited on
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4693260
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End of training

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  1. README.md +13 -8
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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 the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0756
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  - Accuracy: 0.15
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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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- - learning_rate: 0.01
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  - train_batch_size: 64
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  - eval_batch_size: 64
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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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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 10 | 2.1036 | 0.1187 |
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- | No log | 2.0 | 20 | 2.0755 | 0.15 |
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- | No log | 3.0 | 30 | 2.0863 | 0.1062 |
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- | No log | 4.0 | 40 | 2.0900 | 0.1062 |
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- | No log | 5.0 | 50 | 2.0880 | 0.1062 |
 
 
 
 
 
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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 the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1605
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  - Accuracy: 0.15
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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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+ - learning_rate: 0.001
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  - train_batch_size: 64
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  - eval_batch_size: 64
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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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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 10 | 2.3360 | 0.1125 |
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+ | No log | 2.0 | 20 | 2.2480 | 0.1187 |
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+ | No log | 3.0 | 30 | 2.1605 | 0.15 |
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+ | No log | 4.0 | 40 | 2.1227 | 0.15 |
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+ | No log | 5.0 | 50 | 2.1005 | 0.1125 |
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+ | No log | 6.0 | 60 | 2.1004 | 0.1062 |
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+ | No log | 7.0 | 70 | 2.0920 | 0.1062 |
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+ | No log | 8.0 | 80 | 2.0841 | 0.1125 |
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+ | No log | 9.0 | 90 | 2.0897 | 0.1062 |
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+ | No log | 10.0 | 100 | 2.0876 | 0.1062 |
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  ### Framework versions
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