swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50 – AI Model by ahmedALM1221 | AlphaNeural AI

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ahmedALM1221/
swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50

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: 0.6967
Accuracy: 0.7220

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: 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: 0.005
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.97	9	1.6984	0.3729
No log	1.95	18	1.5150	0.4881
1.6944	2.92	27	1.3304	0.5390
1.6944	4.0	37	1.1761	0.6
1.3633	4.97	46	1.0588	0.6373
1.3633	5.95	55	0.9952	0.6475
1.1208	6.92	64	0.9326	0.6610
1.1208	8.0	74	0.8785	0.6712
0.9891	8.97	83	0.8478	0.6746
0.9891	9.95	92	0.8144	0.6847
0.9011	10.92	101	0.7774	0.7017
0.9011	12.0	111	0.7567	0.6983
0.8143	12.97	120	0.7525	0.6949
0.8143	13.95	129	0.7309	0.7051
0.8143	14.92	138	0.7141	0.7119
0.7926	16.0	148	0.7095	0.7186
0.7926	16.97	157	0.7057	0.7220
0.7439	17.95	166	0.6988	0.7220
0.7439	18.92	175	0.6967	0.7220
0.7533	19.46	180	0.6967	0.7220

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3