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ai_vs_real_upscaled_image – AI Model by Timouse | AlphaNeural AI
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Timouse
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ai_vs_real_upscaled_image
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transformers
safetensors
vit
image-classification
generated_from_trainer
google/vit-base-patch16-224-in21k
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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ai_vs_real_upscaled_image
This model is a fine-tuned version of
google/vit-base-patch16-224-in21k
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0489
Accuracy: 0.9846
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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.0672
1.0
2969
0.0489
0.9846
Framework versions
Transformers 4.39.1
Pytorch 2.0.1+cu118
Datasets 2.18.0
Tokenizers 0.15.2
