vit-base-patch16-224-in21k-cards-base-classifier-defects-finder – AI Model by ansilmbabl | AlphaNeural AI

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ansilmbabl/
vit-base-patch16-224-in21k-cards-base-classifier-defects-finder

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image-classification

generated_from_trainer

google/vit-base-patch16-224-in21k

autotrain_compatible

endpoints_compatible

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vit-base-patch16-224-in21k-cards-base-classifier-defects-finder

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0683
Accuracy: 0.999

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4892	0.9929	70	1.3366	0.859
0.4362	2.0	141	0.4142	0.971
0.231	2.9929	211	0.2250	0.988
0.1654	4.0	282	0.1687	0.982
0.1289	4.9929	352	0.1322	0.991
0.0999	6.0	423	0.1184	0.988
0.0824	6.9929	493	0.0852	0.996
0.0789	8.0	564	0.0809	0.998
0.07	8.9929	634	0.0723	0.997
0.067	9.9291	700	0.0683	0.999

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1