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vit-base-patch16-224-finetuned-foveated-features-v2 – AI Model by annazhong | AlphaNeural AI
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annazhong
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vit-base-patch16-224-finetuned-foveated-features-v2
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
google/vit-base-patch16-224
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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vit-base-patch16-224-finetuned-foveated-features-v2
This model is a fine-tuned version of
google/vit-base-patch16-224
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.9396
Accuracy: 0.24
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: 150
eval_batch_size: 150
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 600
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
1
1.9396
0.24
No log
2.0
3
1.9830
0.12
Framework versions
Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.0
Tokenizers 0.13.3
