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vit-Facial-Expression-Recognition – AI Model by lohasingh | AlphaNeural AI
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lohasingh
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vit-Facial-Expression-Recognition
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transformers
safetensors
vit
image-classification
generated_from_trainer
mo-thecreator/vit-Facial-Expression-Recognition
finetune
autotrain_compatible
endpoints_compatible
us
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vit-Facial-Expression-Recognition
This model is a fine-tuned version of
motheecreator/vit-Facial-Expression-Recognition
on the None dataset. It achieves the following results on the evaluation set:
eval_loss: 0.3806
eval_accuracy: 0.8748
eval_runtime: 375.2927
eval_samples_per_second: 78.792
eval_steps_per_second: 2.465
epoch: 0.0433
step: 20
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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 3
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0
