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DiTo97/
swinv2-base-panorama-IQA

Name: swinv2-base-panorama-IQA
Availability: InStock

generated_from_trainer

generator

microsoft/swinv2-base-patch4-window8-256

finetune

apache-2.0

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swinv2-base-panorama-IQA

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 on the isiqa-2019-hf dataset. It achieves the following results on the evaluation set:

Loss: 0.0312
Srocc: 0.1132
Lcc: 0.1583

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: 16
eval_batch_size: 32
seed: 10
gradient_accumulation_steps: 4
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	0.8571	3	0.3021	-0.1668	-0.1392
No log	2.0	7	0.1286	-0.1808	-0.1347
0.2494	2.8571	10	0.0678	-0.1784	-0.1273
0.2494	4.0	14	0.1143	-0.1625	-0.1114
0.2494	4.8571	17	0.0686	-0.1939	-0.1152
0.069	6.0	21	0.0572	-0.2063	-0.1376
0.069	6.8571	24	0.0537	-0.1965	-0.1405
0.069	8.0	28	0.0671	-0.1794	-0.1289
0.0276	8.8571	31	0.0551	-0.1443	-0.1164
0.0276	10.0	35	0.0492	-0.1110	-0.0948
0.0276	10.8571	38	0.0465	-0.0945	-0.0767
0.0181	12.0	42	0.0449	-0.0830	-0.0464
0.0181	12.8571	45	0.0402	-0.0659	-0.0280
0.0181	14.0	49	0.0389	-0.0411	-0.0117
0.0128	14.8571	52	0.0380	-0.0348	-0.0055
0.0128	16.0	56	0.0371	-0.0232	0.0088
0.0128	16.8571	59	0.0360	0.0048	0.0205
0.0112	18.0	63	0.0354	0.0128	0.0385
0.0112	18.8571	66	0.0352	0.0197	0.0509
0.0088	20.0	70	0.0346	0.0331	0.0670
0.0088	20.8571	73	0.0337	0.0412	0.0801
0.0088	22.0	77	0.0347	0.0396	0.0879
0.008	22.8571	80	0.0348	0.0512	0.0954
0.008	24.0	84	0.0339	0.0643	0.1071
0.008	24.8571	87	0.0332	0.0765	0.1143
0.0066	26.0	91	0.0334	0.0855	0.1240
0.0066	26.8571	94	0.0330	0.0938	0.1292
0.0066	28.0	98	0.0317	0.0997	0.1381
0.006	28.8571	101	0.0314	0.1087	0.1432
0.006	30.0	105	0.0317	0.1053	0.1446
0.006	30.8571	108	0.0317	0.0971	0.1465
0.0062	32.0	112	0.0315	0.1032	0.1496
0.0062	32.8571	115	0.0315	0.1032	0.1511
0.0062	34.0	119	0.0314	0.1032	0.1533
0.0057	34.8571	122	0.0314	0.1094	0.1543
0.0057	36.0	126	0.0313	0.1091	0.1558
0.0057	36.8571	129	0.0312	0.1132	0.1570
0.006	38.0	133	0.0312	0.1132	0.1577
0.006	38.8571	136	0.0312	0.1132	0.1581
0.0058	40.0	140	0.0312	0.1132	0.1583
0.0058	40.8571	143	0.0312	0.1132	0.1584
0.0058	42.0	147	0.0312	0.1132	0.1584
0.006	42.8571	150	0.0312	0.1132	0.1584

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1

DiTo97/swinv2-base-panorama-IQA

swinv2-base-panorama-IQA

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DiTo97/
swinv2-base-panorama-IQA