swinv2-tiny-panorama-IQA

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

• Loss: 0.0211
• Srocc: 0.0896
• Lcc: 0.2316

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: 32
• eval_batch_size: 32
• seed: 10
• gradient_accumulation_steps: 2
• 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	0.1747	0.1087	0.1976
No log	2.0	7	0.0570	0.1042	0.1898
0.1599	2.8571	10	0.0320	0.1126	0.1686
0.1599	4.0	14	0.0511	0.0265	0.1235
0.1599	4.8571	17	0.0274	-0.0004	0.1029
0.0602	6.0	21	0.0375	-0.0406	0.0900
0.0602	6.8571	24	0.0306	-0.0533	0.0830
0.0602	8.0	28	0.0255	-0.0726	0.0714
0.029	8.8571	31	0.0247	-0.0568	0.0734
0.029	10.0	35	0.0293	-0.0429	0.0900
0.029	10.8571	38	0.0259	-0.0317	0.0982
0.0199	12.0	42	0.0238	-0.0073	0.1288
0.0199	12.8571	45	0.0243	0.0216	0.1594
0.0199	14.0	49	0.0259	0.0454	0.1810
0.0161	14.8571	52	0.0224	0.0568	0.1954
0.0161	16.0	56	0.0211	0.0896	0.2316
0.0161	16.8571	59	0.0223	0.1001	0.2544
0.0132	18.0	63	0.0217	0.0981	0.2681
0.0132	18.8571	66	0.0221	0.1155	0.2746
0.0103	20.0	70	0.0228	0.1230	0.2831
0.0103	20.8571	73	0.0245	0.1327	0.2944

Framework versions

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