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DiTo97
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swinv2-base-panorama-IQA

Image Classification
TensorBoard
Safetensors
swinv2
vision
Generated from Trainer
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  1. README.md +49 -34
  2. model.safetensors +1 -1
README.md CHANGED
@@ -2,8 +2,6 @@
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  license: apache-2.0
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  base_model: microsoft/swinv2-base-patch4-window8-256
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  tags:
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- - image-classification
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- - vision
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  - generated_from_trainer
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  datasets:
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  - generator
@@ -17,11 +15,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # swinv2-base-panorama-IQA
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- This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the isiqa-2019-hf dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0246
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- - Srocc: 0.0896
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- - Lcc: 0.1773
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  ## Model description
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@@ -40,14 +38,14 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 32
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  - seed: 10
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 50.0
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@@ -55,32 +53,49 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Srocc | Lcc |
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  |:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
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- | No log | 0.8571 | 3 | 0.2685 | -0.1661 | -0.1400 |
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- | No log | 2.0 | 7 | 0.0675 | -0.2071 | -0.1319 |
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- | 0.223 | 2.8571 | 10 | 0.1380 | -0.1972 | -0.1144 |
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- | 0.223 | 4.0 | 14 | 0.0639 | -0.2362 | -0.1162 |
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- | 0.223 | 4.8571 | 17 | 0.0601 | -0.1760 | -0.1097 |
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- | 0.0607 | 6.0 | 21 | 0.0627 | -0.1290 | -0.0852 |
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- | 0.0607 | 6.8571 | 24 | 0.0543 | -0.1050 | -0.0791 |
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- | 0.0607 | 8.0 | 28 | 0.0408 | -0.0683 | -0.0702 |
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- | 0.0212 | 8.8571 | 31 | 0.0419 | -0.0692 | -0.0567 |
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- | 0.0212 | 10.0 | 35 | 0.0343 | -0.0370 | -0.0274 |
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- | 0.0212 | 10.8571 | 38 | 0.0307 | -0.0339 | -0.0013 |
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- | 0.0168 | 12.0 | 42 | 0.0299 | -0.0281 | 0.0233 |
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- | 0.0168 | 12.8571 | 45 | 0.0300 | -0.0428 | 0.0326 |
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- | 0.0168 | 14.0 | 49 | 0.0286 | -0.0238 | 0.0517 |
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- | 0.0143 | 14.8571 | 52 | 0.0283 | -0.0186 | 0.0601 |
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- | 0.0143 | 16.0 | 56 | 0.0273 | -0.0024 | 0.0868 |
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- | 0.0143 | 16.8571 | 59 | 0.0257 | 0.0283 | 0.1119 |
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- | 0.013 | 18.0 | 63 | 0.0247 | 0.0542 | 0.1404 |
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- | 0.013 | 18.8571 | 66 | 0.0247 | 0.0703 | 0.1533 |
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- | 0.0111 | 20.0 | 70 | 0.0246 | 0.0800 | 0.1670 |
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- | 0.0111 | 20.8571 | 73 | 0.0246 | 0.0896 | 0.1773 |
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- | 0.0111 | 22.0 | 77 | 0.0257 | 0.0998 | 0.1835 |
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- | 0.0104 | 22.8571 | 80 | 0.0255 | 0.1017 | 0.1943 |
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- | 0.0104 | 24.0 | 84 | 0.0255 | 0.1149 | 0.2085 |
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- | 0.0104 | 24.8571 | 87 | 0.0255 | 0.1245 | 0.2155 |
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- | 0.0088 | 26.0 | 91 | 0.0262 | 0.1319 | 0.2258 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: microsoft/swinv2-base-patch4-window8-256
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  tags:
 
 
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  - generated_from_trainer
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  datasets:
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  - generator
 
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  # swinv2-base-panorama-IQA
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+ This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0312
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+ - Srocc: 0.1132
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+ - Lcc: 0.1584
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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  - train_batch_size: 16
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  - eval_batch_size: 32
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  - seed: 10
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 50.0
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  | Training Loss | Epoch | Step | Validation Loss | Srocc | Lcc |
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  |:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
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+ | No log | 0.8571 | 3 | 0.3021 | -0.1668 | -0.1392 |
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+ | No log | 2.0 | 7 | 0.1286 | -0.1808 | -0.1347 |
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+ | 0.2494 | 2.8571 | 10 | 0.0678 | -0.1784 | -0.1273 |
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+ | 0.2494 | 4.0 | 14 | 0.1143 | -0.1625 | -0.1114 |
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+ | 0.2494 | 4.8571 | 17 | 0.0686 | -0.1939 | -0.1152 |
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+ | 0.069 | 6.0 | 21 | 0.0572 | -0.2063 | -0.1376 |
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+ | 0.069 | 6.8571 | 24 | 0.0537 | -0.1965 | -0.1405 |
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+ | 0.069 | 8.0 | 28 | 0.0671 | -0.1794 | -0.1289 |
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+ | 0.0276 | 8.8571 | 31 | 0.0551 | -0.1443 | -0.1164 |
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+ | 0.0276 | 10.0 | 35 | 0.0492 | -0.1110 | -0.0948 |
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+ | 0.0276 | 10.8571 | 38 | 0.0465 | -0.0945 | -0.0767 |
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+ | 0.0181 | 12.0 | 42 | 0.0449 | -0.0830 | -0.0464 |
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+ | 0.0181 | 12.8571 | 45 | 0.0402 | -0.0659 | -0.0280 |
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+ | 0.0181 | 14.0 | 49 | 0.0389 | -0.0411 | -0.0117 |
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+ | 0.0128 | 14.8571 | 52 | 0.0380 | -0.0348 | -0.0055 |
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+ | 0.0128 | 16.0 | 56 | 0.0371 | -0.0232 | 0.0088 |
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+ | 0.0128 | 16.8571 | 59 | 0.0360 | 0.0048 | 0.0205 |
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+ | 0.0112 | 18.0 | 63 | 0.0354 | 0.0128 | 0.0385 |
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+ | 0.0112 | 18.8571 | 66 | 0.0352 | 0.0197 | 0.0509 |
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+ | 0.0088 | 20.0 | 70 | 0.0346 | 0.0331 | 0.0670 |
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+ | 0.0088 | 20.8571 | 73 | 0.0337 | 0.0412 | 0.0801 |
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+ | 0.0088 | 22.0 | 77 | 0.0347 | 0.0396 | 0.0879 |
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+ | 0.008 | 22.8571 | 80 | 0.0348 | 0.0512 | 0.0954 |
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+ | 0.008 | 24.0 | 84 | 0.0339 | 0.0643 | 0.1071 |
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+ | 0.008 | 24.8571 | 87 | 0.0332 | 0.0765 | 0.1143 |
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+ | 0.0066 | 26.0 | 91 | 0.0334 | 0.0855 | 0.1240 |
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+ | 0.0066 | 26.8571 | 94 | 0.0330 | 0.0938 | 0.1292 |
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+ | 0.0066 | 28.0 | 98 | 0.0317 | 0.0997 | 0.1381 |
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+ | 0.006 | 28.8571 | 101 | 0.0314 | 0.1087 | 0.1432 |
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+ | 0.006 | 30.0 | 105 | 0.0317 | 0.1053 | 0.1446 |
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+ | 0.006 | 30.8571 | 108 | 0.0317 | 0.0971 | 0.1465 |
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+ | 0.0062 | 32.0 | 112 | 0.0315 | 0.1032 | 0.1496 |
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+ | 0.0062 | 32.8571 | 115 | 0.0315 | 0.1032 | 0.1511 |
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+ | 0.0062 | 34.0 | 119 | 0.0314 | 0.1032 | 0.1533 |
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+ | 0.0057 | 34.8571 | 122 | 0.0314 | 0.1094 | 0.1543 |
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+ | 0.0057 | 36.0 | 126 | 0.0313 | 0.1091 | 0.1558 |
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+ | 0.0057 | 36.8571 | 129 | 0.0312 | 0.1132 | 0.1570 |
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+ | 0.006 | 38.0 | 133 | 0.0312 | 0.1132 | 0.1577 |
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+ | 0.006 | 38.8571 | 136 | 0.0312 | 0.1132 | 0.1581 |
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+ | 0.0058 | 40.0 | 140 | 0.0312 | 0.1132 | 0.1583 |
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+ | 0.0058 | 40.8571 | 143 | 0.0312 | 0.1132 | 0.1584 |
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+ | 0.0058 | 42.0 | 147 | 0.0312 | 0.1132 | 0.1584 |
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+ | 0.006 | 42.8571 | 150 | 0.0312 | 0.1132 | 0.1584 |
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  ### Framework versions
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