swinv2-large-patch4-window12-192-22k-baseline – AI Model by canadianjosieharrison | AlphaNeural AI

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canadianjosieharrison/
swinv2-large-patch4-window12-192-22k-baseline

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image-classification

generated_from_trainer

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

autotrain_compatible

endpoints_compatible

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swinv2-large-patch4-window12-192-22k-baseline

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

Loss: 0.3489
Accuracy: 0.8765

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: 18
eval_batch_size: 18
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 36
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1721	1.0	20	0.8152	0.7407
0.5878	2.0	40	0.4285	0.8395
0.5201	3.0	60	0.5102	0.8148
0.3366	4.0	80	0.3463	0.8519
0.2792	5.0	100	0.4444	0.8272
0.2807	6.0	120	0.3282	0.8765
0.1978	7.0	140	0.3047	0.8642
0.2262	8.0	160	0.4534	0.8765
0.176	9.0	180	0.3605	0.8148
0.17	10.0	200	0.4222	0.8642
0.1445	11.0	220	0.3569	0.9012
0.128	12.0	240	0.4649	0.8642
0.1316	13.0	260	0.3848	0.8765
0.1772	14.0	280	0.4242	0.8395
0.1087	15.0	300	0.3756	0.8889
0.0858	16.0	320	0.4190	0.8519
0.1136	17.0	340	0.4902	0.8765
0.0425	18.0	360	0.3041	0.9012
0.07	19.0	380	0.3456	0.8889
0.0595	20.0	400	0.3489	0.8765

Framework versions

Transformers 4.35.0
Pytorch 2.1.1+cu118
Datasets 2.14.6
Tokenizers 0.14.1