| | --- |
| | license: apache-2.0 |
| | base_model: microsoft/swinv2-tiny-patch4-window8-256 |
| | tags: |
| | - image-classification |
| | - vision |
| | - generated_from_trainer |
| | datasets: |
| | - generator |
| | model-index: |
| | - name: swinv2-tiny-panorama-IQA |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # swinv2-tiny-panorama-IQA |
| |
|
| | This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/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 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.42.3 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| |
|
