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CGIAR-Crop-disease

Image Classification
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Safetensors
swin
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End of training

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  1. README.md +33 -23
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7668
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- - Accuracy: 0.6726
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  ## Model description
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@@ -44,32 +44,42 @@ The following hyperparameters were used during training:
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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_steps: 50
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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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- | 1.047 | 1.0 | 652 | 0.9302 | 0.5817 |
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- | 0.899 | 2.0 | 1304 | 0.8669 | 0.6285 |
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- | 0.8656 | 3.0 | 1956 | 0.8434 | 0.6385 |
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- | 0.8514 | 4.0 | 2608 | 0.8421 | 0.6277 |
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- | 0.8395 | 5.0 | 3260 | 0.8275 | 0.6506 |
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- | 0.832 | 6.0 | 3912 | 0.8444 | 0.6415 |
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- | 0.8065 | 7.0 | 4564 | 0.8204 | 0.6494 |
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- | 0.8031 | 8.0 | 5216 | 0.8271 | 0.6438 |
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- | 0.7954 | 9.0 | 5868 | 0.8025 | 0.6632 |
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- | 0.7939 | 10.0 | 6520 | 0.7917 | 0.6592 |
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- | 0.7893 | 11.0 | 7172 | 0.8043 | 0.6515 |
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- | 0.7731 | 12.0 | 7824 | 0.7878 | 0.6695 |
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- | 0.7759 | 13.0 | 8476 | 0.7806 | 0.6657 |
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- | 0.7676 | 14.0 | 9128 | 0.7816 | 0.6653 |
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- | 0.7605 | 15.0 | 9780 | 0.7882 | 0.6550 |
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- | 0.7566 | 16.0 | 10432 | 0.7881 | 0.6548 |
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- | 0.7554 | 17.0 | 11084 | 0.7824 | 0.6619 |
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- | 0.7384 | 18.0 | 11736 | 0.7668 | 0.6726 |
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- | 0.7442 | 19.0 | 12388 | 0.7830 | 0.6594 |
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- | 0.7296 | 20.0 | 13040 | 0.7709 | 0.6667 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7448
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+ - Accuracy: 0.6857
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  ## Model description
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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_steps: 50
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+ - num_epochs: 30
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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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+ | 0.9931 | 1.0 | 652 | 0.8450 | 0.6346 |
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+ | 0.9034 | 2.0 | 1304 | 0.8367 | 0.6456 |
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+ | 0.8734 | 3.0 | 1956 | 0.8165 | 0.6601 |
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+ | 0.851 | 4.0 | 2608 | 0.8982 | 0.6047 |
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+ | 0.8444 | 5.0 | 3260 | 0.8000 | 0.6626 |
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+ | 0.8261 | 6.0 | 3912 | 0.8339 | 0.6321 |
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+ | 0.8262 | 7.0 | 4564 | 0.7984 | 0.6613 |
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+ | 0.8152 | 8.0 | 5216 | 0.7859 | 0.6740 |
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+ | 0.8081 | 9.0 | 5868 | 0.8387 | 0.6400 |
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+ | 0.8012 | 10.0 | 6520 | 0.8229 | 0.6463 |
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+ | 0.7957 | 11.0 | 7172 | 0.7807 | 0.6715 |
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+ | 0.7975 | 12.0 | 7824 | 0.7752 | 0.6816 |
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+ | 0.7885 | 13.0 | 8476 | 0.7885 | 0.6694 |
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+ | 0.7896 | 14.0 | 9128 | 0.7806 | 0.6774 |
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+ | 0.7871 | 15.0 | 9780 | 0.7713 | 0.6786 |
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+ | 0.7696 | 16.0 | 10432 | 0.7881 | 0.6615 |
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+ | 0.7742 | 17.0 | 11084 | 0.7616 | 0.6797 |
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+ | 0.7638 | 18.0 | 11736 | 0.7509 | 0.6878 |
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+ | 0.7655 | 19.0 | 12388 | 0.7995 | 0.6646 |
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+ | 0.7624 | 20.0 | 13040 | 0.7712 | 0.6768 |
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+ | 0.7544 | 21.0 | 13692 | 0.7491 | 0.6885 |
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+ | 0.7567 | 22.0 | 14344 | 0.7472 | 0.6841 |
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+ | 0.7487 | 23.0 | 14996 | 0.7608 | 0.6818 |
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+ | 0.7427 | 24.0 | 15648 | 0.7494 | 0.6870 |
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+ | 0.7468 | 25.0 | 16300 | 0.7543 | 0.6812 |
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+ | 0.7365 | 26.0 | 16952 | 0.7494 | 0.6855 |
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+ | 0.7328 | 27.0 | 17604 | 0.7448 | 0.6857 |
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+ | 0.7398 | 28.0 | 18256 | 0.7461 | 0.6855 |
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+ | 0.7266 | 29.0 | 18908 | 0.7513 | 0.6822 |
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+ | 0.7286 | 30.0 | 19560 | 0.7456 | 0.6868 |
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  ### Framework versions
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