swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50 – AI Model by ahmedALM1221 | AlphaNeural AI

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ahmedALM1221/
swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50

This model is a fine-tuned version of microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0909
Accuracy: 0.9753

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-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.9
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0236	1.0	122	1.9878	0.1305
1.88	2.0	244	1.7957	0.2867
1.5421	3.0	366	1.3813	0.5149
0.9489	4.0	488	0.9015	0.7030
0.8734	5.0	610	0.6616	0.7667
0.6562	6.0	732	0.5095	0.8140
0.5788	7.0	854	0.4036	0.8520
0.6737	8.0	976	0.3157	0.8921
0.4687	9.0	1098	0.2146	0.9281
0.3775	10.0	1220	0.2020	0.9353
0.3226	11.0	1342	0.1549	0.9558
0.2452	12.0	1464	0.0909	0.9753

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3