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vit-base-patch16-224-food101-v1 – AI Model by ManishW | AlphaNeural AI
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ManishW
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vit-base-patch16-224-food101-v1
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
food101
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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vit-base-patch16-224-food101-v1
This model is a fine-tuned version of
google/vit-base-patch16-224
on the food101 dataset. It achieves the following results on the evaluation set:
Loss: 0.2359
Accuracy: 0.924
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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.0682
0.99
31
0.3073
0.908
0.0425
1.98
62
0.2663
0.915
0.0262
2.98
93
0.2173
0.928
0.0446
4.0
125
0.2195
0.937
0.0642
4.96
155
0.2359
0.924
Framework versions
Transformers 4.28.0
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3
