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mnist-digit-recognition – AI Model by Fadri | AlphaNeural AI
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Fadri
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mnist-digit-recognition
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transformers
tensorboard
safetensors
image-classification
mnist
handwritten-digits
pytorch
generated_from_trainer
endpoints_compatible
us
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mnist-digit-recognition
This model is a fine-tuned version of
scratch
on the mnist dataset. It achieves the following results on the evaluation set:
Loss: 0.0181
Accuracy: 0.9943
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: 0.0003
train_batch_size: 128
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.0684
1.0
422
0.0517
0.9853
0.0451
2.0
844
0.0343
0.9888
0.039
3.0
1266
0.0289
0.9907
0.0296
4.0
1688
0.0280
0.9917
0.0242
5.0
2110
0.0264
0.9918
Framework versions
Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1
