Train-Test-Augmentation-V5-beit-base – AI Model by ahmedesmail16 | AlphaNeural AI

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Train-Test-Augmentation-V5-beit-base

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image-classification

generated_from_trainer

microsoft/beit-base-patch16-224-pt22k-ft22k

autotrain_compatible

endpoints_compatible

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Train-Test-Augmentation-V5-beit-base

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6899
Accuracy: 0.8442

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0473	1.0	55	0.8312	0.7759
0.3767	2.0	110	0.5476	0.8336
0.176	3.0	165	0.5248	0.8256
0.07	4.0	220	0.5597	0.8527
0.043	5.0	275	0.5707	0.8472
0.0272	6.0	330	0.6225	0.8264
0.0168	7.0	385	0.5721	0.8553
0.0076	8.0	440	0.5967	0.8608
0.006	9.0	495	0.7036	0.8272
0.007	10.0	550	0.7167	0.8400
0.0048	11.0	605	0.6734	0.8506
0.0023	12.0	660	0.7424	0.8332
0.0032	13.0	715	0.7283	0.8340
0.002	14.0	770	0.6805	0.8502
0.0021	15.0	825	0.6899	0.8442

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.15.2