This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0190

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.84	4	0.6834
No log	1.89	9	0.4899
0.6773	2.95	14	0.2752
0.6773	4.0	19	0.1311
0.3528	4.84	23	0.0812
0.3528	5.89	28	0.0477
0.1595	6.95	33	0.0289
0.1595	8.0	38	0.0229
0.1054	8.84	42	0.0213
0.1054	9.89	47	0.0281
0.0822	10.95	52	0.0190
0.0822	12.0	57	0.0240
0.076	12.63	60	0.0237

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

t1msan/swinv2-tiny-patch4-window8-256-Kontur-competition-1.3K

swinv2-tiny-patch4-window8-256-Kontur-competition-1.3K

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

t1msan/
swinv2-tiny-patch4-window8-256-Kontur-competition-1.3K