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swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals

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  1. README.md +49 -35
  2. model.safetensors +1 -1
README.md CHANGED
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  license: apache-2.0
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  base_model: microsoft/swinv2-tiny-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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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals
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  results: []
@@ -17,10 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals
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- 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 matthieulel/galaxy10_decals dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4357
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- - Accuracy: 0.8585
 
 
 
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  ## Model description
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@@ -40,45 +44,55 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 128
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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: 20
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | 1.318 | 0.9940 | 124 | 1.0409 | 0.6359 |
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- | 0.9268 | 1.9960 | 249 | 0.7164 | 0.7497 |
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- | 0.8221 | 2.9980 | 374 | 0.6210 | 0.7875 |
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- | 0.7276 | 4.0 | 499 | 0.5564 | 0.8162 |
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- | 0.6425 | 4.9940 | 623 | 0.5226 | 0.8162 |
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- | 0.6518 | 5.9960 | 748 | 0.5377 | 0.8185 |
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- | 0.6096 | 6.9980 | 873 | 0.5341 | 0.8219 |
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- | 0.6282 | 8.0 | 998 | 0.4718 | 0.8399 |
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- | 0.5394 | 8.9940 | 1122 | 0.5113 | 0.8281 |
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- | 0.5718 | 9.9960 | 1247 | 0.5019 | 0.8292 |
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- | 0.5507 | 10.9980 | 1372 | 0.4545 | 0.8461 |
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- | 0.4921 | 12.0 | 1497 | 0.4613 | 0.8416 |
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- | 0.5571 | 12.9940 | 1621 | 0.4587 | 0.8416 |
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- | 0.512 | 13.9960 | 1746 | 0.4673 | 0.8512 |
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- | 0.4855 | 14.9980 | 1871 | 0.4641 | 0.8489 |
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- | 0.4895 | 16.0 | 1996 | 0.4556 | 0.8450 |
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- | 0.4809 | 16.9940 | 2120 | 0.4317 | 0.8523 |
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- | 0.4785 | 17.9960 | 2245 | 0.4338 | 0.8534 |
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- | 0.444 | 18.9980 | 2370 | 0.4357 | 0.8579 |
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- | 0.4255 | 19.8798 | 2480 | 0.4357 | 0.8585 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.40.1
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- - Pytorch 1.12.1+cu116
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- - Datasets 2.19.0
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- - Tokenizers 0.19.1
 
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  license: apache-2.0
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  base_model: microsoft/swinv2-tiny-patch4-window8-256
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  tags:
 
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - precision
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+ - recall
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+ - f1
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  model-index:
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  - name: swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals
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  results: []
 
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  # swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals
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+ This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4539
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+ - Accuracy: 0.8551
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+ - Precision: 0.8533
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+ - Recall: 0.8551
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+ - F1: 0.8516
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 256
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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: 30
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 1.7462 | 0.99 | 62 | 1.4592 | 0.4431 | 0.4309 | 0.4431 | 0.3967 |
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+ | 1.1805 | 2.0 | 125 | 1.0335 | 0.6460 | 0.6741 | 0.6460 | 0.6241 |
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+ | 0.9342 | 2.99 | 187 | 0.7051 | 0.7537 | 0.7478 | 0.7537 | 0.7394 |
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+ | 0.786 | 4.0 | 250 | 0.6468 | 0.7745 | 0.7731 | 0.7745 | 0.7637 |
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+ | 0.7062 | 4.99 | 312 | 0.6013 | 0.8038 | 0.8052 | 0.8038 | 0.8008 |
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+ | 0.7011 | 6.0 | 375 | 0.5373 | 0.8123 | 0.8171 | 0.8123 | 0.8041 |
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+ | 0.7014 | 6.99 | 437 | 0.5470 | 0.8044 | 0.8048 | 0.8044 | 0.7995 |
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+ | 0.6447 | 8.0 | 500 | 0.5309 | 0.8083 | 0.8087 | 0.8083 | 0.8025 |
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+ | 0.608 | 8.99 | 562 | 0.4836 | 0.8337 | 0.8323 | 0.8337 | 0.8300 |
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+ | 0.6196 | 10.0 | 625 | 0.4797 | 0.8331 | 0.8293 | 0.8331 | 0.8268 |
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+ | 0.6031 | 10.99 | 687 | 0.4863 | 0.8264 | 0.8274 | 0.8264 | 0.8239 |
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+ | 0.5462 | 12.0 | 750 | 0.4749 | 0.8354 | 0.8341 | 0.8354 | 0.8313 |
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+ | 0.5868 | 12.99 | 812 | 0.5269 | 0.8236 | 0.8268 | 0.8236 | 0.8171 |
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+ | 0.5844 | 14.0 | 875 | 0.4402 | 0.8472 | 0.8447 | 0.8472 | 0.8430 |
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+ | 0.5326 | 14.99 | 937 | 0.4635 | 0.8393 | 0.8359 | 0.8393 | 0.8353 |
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+ | 0.5313 | 16.0 | 1000 | 0.4734 | 0.8365 | 0.8345 | 0.8365 | 0.8300 |
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+ | 0.4893 | 16.99 | 1062 | 0.4675 | 0.8365 | 0.8335 | 0.8365 | 0.8316 |
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+ | 0.4983 | 18.0 | 1125 | 0.4441 | 0.8444 | 0.8431 | 0.8444 | 0.8401 |
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+ | 0.518 | 18.99 | 1187 | 0.4693 | 0.8416 | 0.8441 | 0.8416 | 0.8376 |
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+ | 0.5228 | 20.0 | 1250 | 0.4732 | 0.8410 | 0.8379 | 0.8410 | 0.8358 |
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+ | 0.4761 | 20.99 | 1312 | 0.4567 | 0.8489 | 0.8493 | 0.8489 | 0.8460 |
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+ | 0.5311 | 22.0 | 1375 | 0.4582 | 0.8484 | 0.8469 | 0.8484 | 0.8433 |
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+ | 0.4894 | 22.99 | 1437 | 0.4627 | 0.8467 | 0.8450 | 0.8467 | 0.8433 |
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+ | 0.4791 | 24.0 | 1500 | 0.4580 | 0.8506 | 0.8493 | 0.8506 | 0.8481 |
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+ | 0.479 | 24.99 | 1562 | 0.4625 | 0.8472 | 0.8443 | 0.8472 | 0.8433 |
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+ | 0.4487 | 26.0 | 1625 | 0.4557 | 0.8495 | 0.8469 | 0.8495 | 0.8447 |
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+ | 0.4515 | 26.99 | 1687 | 0.4501 | 0.8534 | 0.8510 | 0.8534 | 0.8500 |
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+ | 0.4862 | 28.0 | 1750 | 0.4552 | 0.8551 | 0.8529 | 0.8551 | 0.8513 |
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+ | 0.4348 | 28.99 | 1812 | 0.4512 | 0.8506 | 0.8486 | 0.8506 | 0.8469 |
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+ | 0.4623 | 29.76 | 1860 | 0.4539 | 0.8551 | 0.8533 | 0.8551 | 0.8516 |
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  ### Framework versions
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+ - Transformers 4.37.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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