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rt-detr-v2_barcode-detection

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rt_detr_v2
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -16,21 +16,21 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [PekingU/rtdetr_v2_r18vd](https://huggingface.co/PekingU/rtdetr_v2_r18vd) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 5.0116
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- - Map: 0.6843
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- - Map 50: 0.8399
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- - Map 75: 0.7445
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- - Map Small: 0.3026
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- - Map Medium: 0.7126
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- - Map Large: 0.7607
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- - Mar 1: 0.371
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- - Mar 10: 0.8374
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- - Mar 100: 0.8664
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- - Mar Small: 0.6176
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- - Mar Medium: 0.8646
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- - Mar Large: 0.8864
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- - Map Barcode: 0.6843
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- - Mar 100 Barcode: 0.8664
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  ## Model description
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@@ -56,18 +56,20 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 300
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- - num_epochs: 6
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Barcode | Map Large | Map Medium | Map Small | Mar 1 | Mar 10 | Mar 100 | Mar 100 Barcode | Mar Large | Mar Medium | Mar Small |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-----------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------------:|:---------:|:----------:|:---------:|
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- | 7.43 | 1.0 | 1636 | 5.0547 | 0.623 | 0.7756 | 0.6841 | 0.623 | 0.6594 | 0.6674 | 0.2648 | 0.3542 | 0.8117 | 0.8521 | 0.8521 | 0.8749 | 0.8552 | 0.5325 |
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- | 6.9538 | 2.0 | 3272 | 4.9508 | 0.6297 | 0.7829 | 0.6878 | 0.6297 | 0.7109 | 0.6449 | 0.261 | 0.3612 | 0.8238 | 0.8604 | 0.8604 | 0.8794 | 0.8624 | 0.5972 |
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- | 6.6733 | 3.0 | 4908 | 5.0056 | 0.6553 | 0.8073 | 0.7122 | 0.6553 | 0.7359 | 0.6782 | 0.2319 | 0.3481 | 0.8201 | 0.8542 | 0.8542 | 0.8712 | 0.8573 | 0.6107 |
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- | 6.5783 | 4.0 | 6544 | 5.0493 | 0.6524 | 0.808 | 0.711 | 0.6524 | 0.7472 | 0.6782 | 0.2391 | 0.3581 | 0.8268 | 0.8629 | 0.8629 | 0.8819 | 0.8639 | 0.6066 |
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- | 6.4986 | 5.0 | 8180 | 5.0208 | 0.6944 | 0.8578 | 0.7542 | 0.6944 | 0.7651 | 0.7135 | 0.2808 | 0.3706 | 0.8307 | 0.8611 | 0.8611 | 0.879 | 0.863 | 0.6131 |
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- | 6.4056 | 6.0 | 9816 | 5.0116 | 0.6843 | 0.8399 | 0.7445 | 0.3026 | 0.7126 | 0.7607 | 0.371 | 0.8374 | 0.8664 | 0.6176 | 0.8646 | 0.8864 | 0.6843 | 0.8664 |
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [PekingU/rtdetr_v2_r18vd](https://huggingface.co/PekingU/rtdetr_v2_r18vd) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 5.0340
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+ - Map: 0.6583
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+ - Map 50: 0.8036
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+ - Map 75: 0.7156
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+ - Map Small: 0.2754
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+ - Map Medium: 0.7152
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+ - Map Large: 0.747
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+ - Mar 1: 0.3589
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+ - Mar 10: 0.833
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+ - Mar 100: 0.8673
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+ - Mar Small: 0.6183
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+ - Mar Medium: 0.8656
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+ - Mar Large: 0.8872
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+ - Map Barcode: 0.6583
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+ - Mar 100 Barcode: 0.8673
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  ## Model description
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 300
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+ - num_epochs: 8
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Barcode | Map Large | Map Medium | Map Small | Mar 1 | Mar 10 | Mar 100 | Mar 100 Barcode | Mar Large | Mar Medium | Mar Small |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-----------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------------:|:---------:|:----------:|:---------:|
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+ | 7.43 | 1.0 | 1636 | 5.0547 | 0.623 | 0.7756 | 0.6841 | 0.623 | 0.6594 | 0.6674 | 0.2648 | 0.3542 | 0.8117 | 0.8521 | 0.8521 | 0.8749 | 0.8552 | 0.5325 |
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+ | 6.9538 | 2.0 | 3272 | 4.9508 | 0.6297 | 0.7829 | 0.6878 | 0.6297 | 0.7109 | 0.6449 | 0.261 | 0.3612 | 0.8238 | 0.8604 | 0.8604 | 0.8794 | 0.8624 | 0.5972 |
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+ | 6.6733 | 3.0 | 4908 | 5.0056 | 0.6553 | 0.8073 | 0.7122 | 0.6553 | 0.7359 | 0.6782 | 0.2319 | 0.3481 | 0.8201 | 0.8542 | 0.8542 | 0.8712 | 0.8573 | 0.6107 |
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+ | 6.5783 | 4.0 | 6544 | 5.0493 | 0.6524 | 0.808 | 0.711 | 0.6524 | 0.7472 | 0.6782 | 0.2391 | 0.3581 | 0.8268 | 0.8629 | 0.8629 | 0.8819 | 0.8639 | 0.6066 |
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+ | 6.4986 | 5.0 | 8180 | 5.0208 | 0.6944 | 0.8578 | 0.7542 | 0.6944 | 0.7651 | 0.7135 | 0.2808 | 0.3706 | 0.8307 | 0.8611 | 0.8611 | 0.879 | 0.863 | 0.6131 |
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+ | 6.4056 | 6.0 | 9816 | 5.0116 | 0.6843 | 0.8399 | 0.7445 | 0.6843 | 0.7607 | 0.7126 | 0.3026 | 0.371 | 0.8374 | 0.8664 | 0.8664 | 0.8864 | 0.8646 | 0.6176 |
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+ | 6.4074 | 7.0 | 11452 | 5.0229 | 0.653 | 0.7989 | 0.7106 | 0.2768 | 0.7112 | 0.731 | 0.3513 | 0.8326 | 0.8694 | 0.619 | 0.8675 | 0.8896 | 0.653 | 0.8694 |
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+ | 6.2576 | 8.0 | 13088 | 5.0340 | 0.6583 | 0.8036 | 0.7156 | 0.2754 | 0.7152 | 0.747 | 0.3589 | 0.833 | 0.8673 | 0.6183 | 0.8656 | 0.8872 | 0.6583 | 0.8673 |
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  ### Framework versions
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