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kensvin
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emotion_classification

Image Classification
Transformers
PyTorch
vit
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +27 -10
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.11875
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ 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 the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0823
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- - Accuracy: 0.1187
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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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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- - num_epochs: 3
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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 | 80 | 2.0822 | 0.1062 |
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- | No log | 2.0 | 160 | 2.0839 | 0.1125 |
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- | No log | 3.0 | 240 | 2.0825 | 0.1187 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.33.1
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.60625
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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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: 1.2024
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+ - Accuracy: 0.6062
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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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: 20
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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 | 1.3600 | 0.4938 |
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+ | No log | 2.0 | 20 | 1.2908 | 0.4938 |
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+ | No log | 3.0 | 30 | 1.2799 | 0.5 |
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+ | No log | 4.0 | 40 | 1.2110 | 0.5312 |
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+ | No log | 5.0 | 50 | 1.2178 | 0.5188 |
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+ | No log | 6.0 | 60 | 1.2189 | 0.5188 |
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+ | No log | 7.0 | 70 | 1.2566 | 0.5375 |
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+ | No log | 8.0 | 80 | 1.1838 | 0.5687 |
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+ | No log | 9.0 | 90 | 1.2730 | 0.55 |
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+ | No log | 10.0 | 100 | 1.2329 | 0.575 |
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+ | No log | 11.0 | 110 | 1.2224 | 0.5563 |
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+ | No log | 12.0 | 120 | 1.2729 | 0.5563 |
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+ | No log | 13.0 | 130 | 1.2678 | 0.5687 |
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+ | No log | 14.0 | 140 | 1.2423 | 0.5687 |
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+ | No log | 15.0 | 150 | 1.1704 | 0.6312 |
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+ | No log | 16.0 | 160 | 1.2925 | 0.5625 |
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+ | No log | 17.0 | 170 | 1.3557 | 0.5312 |
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+ | No log | 18.0 | 180 | 1.2951 | 0.5687 |
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+ | No log | 19.0 | 190 | 1.2594 | 0.5625 |
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+ | No log | 20.0 | 200 | 1.2463 | 0.5687 |
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  ### Framework versions
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+ - Transformers 4.33.2
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
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