swinv2-small-panorama-IQA
This model is a fine-tuned version of microsoft/swinv2-small-patch4-window16-256 on the isiqa-2019-hf dataset. It achieves the following results on the evaluation set:
- Loss: 0.0223
- Srocc: 0.1291
- Lcc: 0.1271
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 | 0.2948 | -0.3890 | -0.3824 |
| No log | 2.0 | 7 | 0.1143 | -0.3665 | -0.3732 |
| 0.1552 | 2.8571 | 10 | 0.0768 | -0.3477 | -0.3657 |
| 0.1552 | 4.0 | 14 | 0.0748 | -0.3395 | -0.3504 |
| 0.1552 | 4.8571 | 17 | 0.0517 | -0.3498 | -0.3322 |
| 0.0657 | 6.0 | 21 | 0.0553 | -0.3337 | -0.3060 |
| 0.0657 | 6.8571 | 24 | 0.0434 | -0.2921 | -0.2810 |
| 0.0657 | 8.0 | 28 | 0.0406 | -0.2481 | -0.2570 |
| 0.0249 | 8.8571 | 31 | 0.0402 | -0.2346 | -0.2478 |
| 0.0249 | 10.0 | 35 | 0.0384 | -0.2076 | -0.2182 |
| 0.0249 | 10.8571 | 38 | 0.0317 | -0.1919 | -0.1923 |
| 0.0215 | 12.0 | 42 | 0.0310 | -0.1518 | -0.1636 |
| 0.0215 | 12.8571 | 45 | 0.0317 | -0.1291 | -0.1549 |
| 0.0215 | 14.0 | 49 | 0.0301 | -0.0975 | -0.1292 |
| 0.0154 | 14.8571 | 52 | 0.0285 | -0.0804 | -0.1057 |
| 0.0154 | 16.0 | 56 | 0.0277 | -0.0461 | -0.0762 |
| 0.0154 | 16.8571 | 59 | 0.0263 | -0.0357 | -0.0485 |
| 0.0128 | 18.0 | 63 | 0.0263 | -0.0171 | -0.0317 |
| 0.0128 | 18.8571 | 66 | 0.0265 | -0.0040 | -0.0236 |
| 0.0113 | 20.0 | 70 | 0.0263 | 0.0227 | -0.0089 |
| 0.0113 | 20.8571 | 73 | 0.0256 | 0.0254 | 0.0081 |
| 0.0113 | 22.0 | 77 | 0.0249 | 0.0493 | 0.0233 |
| 0.0104 | 22.8571 | 80 | 0.0246 | 0.0616 | 0.0330 |
| 0.0104 | 24.0 | 84 | 0.0242 | 0.0691 | 0.0435 |
| 0.0104 | 24.8571 | 87 | 0.0240 | 0.0796 | 0.0518 |
| 0.0095 | 26.0 | 91 | 0.0238 | 0.0830 | 0.0679 |
| 0.0095 | 26.8571 | 94 | 0.0235 | 0.0929 | 0.0747 |
| 0.0095 | 28.0 | 98 | 0.0232 | 0.1003 | 0.0862 |
| 0.009 | 28.8571 | 101 | 0.0229 | 0.1050 | 0.0955 |
| 0.009 | 30.0 | 105 | 0.0226 | 0.1072 | 0.1052 |
| 0.009 | 30.8571 | 108 | 0.0226 | 0.1177 | 0.1110 |
| 0.0084 | 32.0 | 112 | 0.0225 | 0.1286 | 0.1152 |
| 0.0084 | 32.8571 | 115 | 0.0224 | 0.1296 | 0.1167 |
| 0.0084 | 34.0 | 119 | 0.0224 | 0.1296 | 0.1185 |
| 0.0085 | 34.8571 | 122 | 0.0224 | 0.1310 | 0.1200 |
| 0.0085 | 36.0 | 126 | 0.0224 | 0.1263 | 0.1221 |
| 0.0085 | 36.8571 | 129 | 0.0224 | 0.1249 | 0.1233 |
| 0.0082 | 38.0 | 133 | 0.0223 | 0.1272 | 0.1247 |
| 0.0082 | 38.8571 | 136 | 0.0223 | 0.1272 | 0.1255 |
| 0.008 | 40.0 | 140 | 0.0223 | 0.1291 | 0.1265 |
| 0.008 | 40.8571 | 143 | 0.0223 | 0.1291 | 0.1269 |
| 0.008 | 42.0 | 147 | 0.0223 | 0.1291 | 0.1271 |
| 0.0078 | 42.8571 | 150 | 0.0223 | 0.1291 | 0.1271 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1