pvc-quality-swinv2-base – AI Model by p1atdev | AlphaNeural AI

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p1atdev/
pvc-quality-swinv2-base

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image-classification

generated_from_trainer

microsoft/swinv2-base-patch4-window12-192-22k

autotrain_compatible

endpoints_compatible

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pvc-quality-swinv2-base

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on the pvc figure images dataset. It achieves the following results on the evaluation set:

Loss: 1.2396
Accuracy: 0.5317

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7254	0.98	39	1.4826	0.4109
1.3316	1.99	79	1.2177	0.5136
1.0864	2.99	119	1.3006	0.4653
0.8572	4.0	159	1.2090	0.5015
0.7466	4.98	198	1.2150	0.5378
0.5986	5.99	238	1.4600	0.4955
0.4784	6.99	278	1.4131	0.5196
0.3525	8.0	318	1.5256	0.4985
0.3472	8.98	357	1.3883	0.5166
0.3281	9.81	390	1.5012	0.4955

Framework versions

Transformers 4.34.1
Pytorch 2.0.0+cu118
Datasets 2.14.5
Tokenizers 0.14.0