This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3204
Accuracy: 0.8081
F1: 0.7504
Precision: 0.6851
Recall: 0.8295

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: 3e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6377	1.0	143	0.6156	0.6421	0.5356	0.4881	0.5934
0.4556	2.0	286	0.4928	0.7141	0.6286	0.5734	0.6957
0.3616	3.0	429	0.5772	0.6895	0.5930	0.5450	0.6503
0.3582	4.0	572	0.3441	0.7835	0.6644	0.7208	0.6162
0.3374	5.0	715	0.4094	0.7699	0.7434	0.6072	0.9583
0.3273	6.0	858	0.6065	0.7115	0.6364	0.5665	0.7260
0.3091	7.0	1001	0.3204	0.8081	0.7504	0.6851	0.8295
0.3026	8.0	1144	0.3946	0.7694	0.6120	0.7380	0.5227

Framework versions

Transformers 4.46.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

LynnKukunda/swin-tiny-patch4-window7-224-image-classifier

swin-tiny-patch4-window7-224-image-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

LynnKukunda/
swin-tiny-patch4-window7-224-image-classifier