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riyadifirman
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klasifikasiburung

Image Classification
Transformers
Safetensors
resnet
Generated from Trainer
Inference Endpoints
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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [RobertZ2011/resnet-18-birb](https://huggingface.co/RobertZ2011/resnet-18-birb) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9598
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- - Accuracy: 0.7706
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- - Precision: 0.7767
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- - Recall: 0.7706
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- - F1: 0.7692
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  ## Model description
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@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 1.5486 | 1.0 | 188 | 1.3741 | 0.7408 | 0.7590 | 0.7408 | 0.7332 |
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- | 1.0796 | 2.0 | 376 | 1.2264 | 0.7558 | 0.7626 | 0.7558 | 0.7497 |
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- | 0.7272 | 3.0 | 564 | 1.1335 | 0.7642 | 0.7740 | 0.7642 | 0.7615 |
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- | 0.5171 | 4.0 | 752 | 1.0920 | 0.7701 | 0.7750 | 0.7701 | 0.7669 |
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- | 0.4038 | 5.0 | 940 | 1.0484 | 0.7698 | 0.7765 | 0.7698 | 0.7679 |
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- | 0.2805 | 6.0 | 1128 | 1.0332 | 0.7722 | 0.7810 | 0.7722 | 0.7710 |
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- | 0.1952 | 7.0 | 1316 | 1.0093 | 0.7701 | 0.7757 | 0.7701 | 0.7686 |
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- | 0.1279 | 8.0 | 1504 | 1.0021 | 0.7706 | 0.7780 | 0.7706 | 0.7693 |
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- | 0.0787 | 9.0 | 1692 | 0.9884 | 0.7687 | 0.7747 | 0.7687 | 0.7673 |
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- | 0.0767 | 10.0 | 1880 | 0.9691 | 0.7682 | 0.7738 | 0.7682 | 0.7670 |
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- | 0.0574 | 11.0 | 2068 | 0.9791 | 0.7694 | 0.7741 | 0.7694 | 0.7673 |
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- | 0.0423 | 12.0 | 2256 | 0.9699 | 0.7698 | 0.7760 | 0.7698 | 0.7685 |
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- | 0.0361 | 13.0 | 2444 | 0.9650 | 0.7684 | 0.7731 | 0.7684 | 0.7666 |
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- | 0.0398 | 14.0 | 2632 | 0.9705 | 0.7675 | 0.7742 | 0.7675 | 0.7666 |
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- | 0.0441 | 15.0 | 2820 | 0.9598 | 0.7706 | 0.7767 | 0.7706 | 0.7692 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [RobertZ2011/resnet-18-birb](https://huggingface.co/RobertZ2011/resnet-18-birb) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0186
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+ - Accuracy: 0.7565
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+ - Precision: 0.7631
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+ - Recall: 0.7565
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+ - F1: 0.7554
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 1.5955 | 1.0 | 188 | 1.4442 | 0.7235 | 0.7426 | 0.7235 | 0.7169 |
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+ | 1.1224 | 2.0 | 376 | 1.2881 | 0.7458 | 0.7546 | 0.7458 | 0.7406 |
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+ | 0.7778 | 3.0 | 564 | 1.1965 | 0.7501 | 0.7635 | 0.7501 | 0.7483 |
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+ | 0.5573 | 4.0 | 752 | 1.1417 | 0.7565 | 0.7635 | 0.7565 | 0.7538 |
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+ | 0.4231 | 5.0 | 940 | 1.1077 | 0.7584 | 0.7671 | 0.7584 | 0.7567 |
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+ | 0.2878 | 6.0 | 1128 | 1.0893 | 0.7601 | 0.7716 | 0.7601 | 0.7597 |
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+ | 0.2043 | 7.0 | 1316 | 1.0688 | 0.7591 | 0.7661 | 0.7591 | 0.7579 |
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+ | 0.1326 | 8.0 | 1504 | 1.0687 | 0.7582 | 0.7653 | 0.7582 | 0.7565 |
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+ | 0.0851 | 9.0 | 1692 | 1.0502 | 0.7598 | 0.7652 | 0.7598 | 0.7581 |
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+ | 0.0807 | 10.0 | 1880 | 1.0318 | 0.7582 | 0.7644 | 0.7582 | 0.7569 |
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+ | 0.0581 | 11.0 | 2068 | 1.0403 | 0.7572 | 0.7629 | 0.7572 | 0.7558 |
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+ | 0.043 | 12.0 | 2256 | 1.0295 | 0.7565 | 0.7633 | 0.7565 | 0.7557 |
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+ | 0.0379 | 13.0 | 2444 | 1.0271 | 0.7568 | 0.7636 | 0.7568 | 0.7557 |
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+ | 0.0399 | 14.0 | 2632 | 1.0319 | 0.7558 | 0.7627 | 0.7558 | 0.7549 |
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+ | 0.0447 | 15.0 | 2820 | 1.0186 | 0.7565 | 0.7631 | 0.7565 | 0.7554 |
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  ### Framework versions