swinv2-large-panorama-IQA

This model is a fine-tuned version of microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft on the isiqa-2019-hf dataset. It achieves the following results on the evaluation set:

• Loss: 0.0352
• Srocc: 0.0683
• Lcc: 0.1820

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: 1e-05
• train_batch_size: 4
• eval_batch_size: 32
• seed: 10
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50.0

Training results

Training Loss	Epoch	Step	Validation Loss	Srocc	Lcc
No log	0.8889	3	0.4401	-0.1586	-0.1147
No log	1.7778	6	0.2206	-0.2675	-0.1545
0.3084	2.9630	10	0.1910	-0.2754	-0.1813
0.3084	3.8519	13	0.2334	-0.2170	-0.1511
0.3084	4.7407	16	0.1484	-0.2	-0.1310
0.0852	5.9259	20	0.1259	-0.1021	-0.0852
0.0852	6.8148	23	0.1552	-0.0709	-0.0595
0.0852	8.0	27	0.0942	-0.0948	-0.0584
0.0406	8.8889	30	0.0841	-0.0480	-0.0550
0.0406	9.7778	33	0.0886	-0.0576	-0.0448
0.0406	10.9630	37	0.0721	-0.0773	-0.0474
0.023	11.8519	40	0.0697	-0.0446	-0.0364
0.023	12.7407	43	0.0577	-0.0217	-0.0091
0.023	13.9259	47	0.0666	-0.0314	0.0112
0.0136	14.8148	50	0.0525	-0.0501	0.0060
0.0136	16.0	54	0.0626	-0.0178	0.0504
0.0136	16.8889	57	0.0438	0.0159	0.0827
0.0113	17.7778	60	0.0503	0.0741	0.1074
0.0113	18.9630	64	0.0429	0.0818	0.1129
0.0113	19.8519	67	0.0455	0.0874	0.1188
0.0097	20.7407	70	0.0597	0.0926	0.1316
0.0097	21.9259	74	0.0397	0.0614	0.1446
0.0097	22.8148	77	0.0529	0.0778	0.1637
0.0084	24.0	81	0.0366	0.0716	0.1761
0.0084	24.8889	84	0.0352	0.0683	0.1820
0.0084	25.7778	87	0.0491	0.0970	0.1848
0.0078	26.9630	91	0.0396	0.0984	0.1831
0.0078	27.8519	94	0.0395	0.1012	0.1856
0.0078	28.7407	97	0.0426	0.1097	0.1956
0.0063	29.9259	101	0.0370	0.1002	0.1984

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1