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ViT-chess-V4 – AI Model by Migga | AlphaNeural AI
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Migga
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ViT-chess-V4
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transformers
pytorch
vit
image-classification
generated_from_trainer
autotrain_compatible
endpoints_compatible
us
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ViT-chess-V4
This model is a fine-tuned version of
on the None dataset. It achieves the following results on the evaluation set:
Loss: 4.2867
Accuracy: 0.1942
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: 2e-05
train_batch_size: 10
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
5.4877
1.0
45000
5.4554
0.1044
4.9794
2.0
90000
5.0001
0.1371
4.5956
3.0
135000
4.6720
0.1596
4.3402
4.0
180000
4.4082
0.1834
4.097
5.0
225000
4.2867
0.1942
Framework versions
Transformers 4.18.0
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1
