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vit_for_dfl – AI Model by ManuD | AlphaNeural AI
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ManuD
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vit_for_dfl
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
dfl
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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vit_for_dfl
This model is a fine-tuned version of
google/vit-base-patch16-224-in21k
on the dfl dataset. It achieves the following results on the evaluation set:
Loss: 0.1771
F1: 0.2453
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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
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
F1
0.0836
1.0
358
0.1841
0.2453
0.207
2.0
716
0.1835
0.2453
0.2325
3.0
1074
0.1771
0.2453
Framework versions
Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2
