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vit-emotion-output – AI Model by tialdrine | AlphaNeural AI
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tialdrine
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vit-emotion-output
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transformers
safetensors
vit
image-classification
generated_from_trainer
imagefolder
google/vit-base-patch16-224
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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vit-emotion-output
This model is a fine-tuned version of
google/vit-base-patch16-224
on the imagefolder dataset. It achieves the following results on the evaluation set:
Loss: 1.5533
Accuracy: 0.4
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
1.8721
1.0
40
1.8115
0.3125
1.3538
2.0
80
1.6587
0.3312
1.1398
3.0
120
1.5723
0.3875
1.0086
4.0
160
1.5533
0.4
Framework versions
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
