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vit-base-molecul – AI Model by dyvapandhu | AlphaNeural AI
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dyvapandhu
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vit-base-molecul
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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vit-base-molecul
This model is a fine-tuned version of
google/vit-base-patch16-224
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.6052
Accuracy: 0.69
F1: 0.6900
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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.4633
3.85
50
0.6052
0.69
0.6900
0.0341
7.69
100
0.8705
0.71
0.7100
Framework versions
Transformers 4.31.0.dev0
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.11.0
