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cems-official
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panels_detection_rtdetr

Object Detection
Transformers
Safetensors
rt_detr
Generated from Trainer
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  1. README.md +106 -0
  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: PekingU/rtdetr_r101vd_coco_o365
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: panels_detection_rtdetr
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # panels_detection_rtdetr
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+
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+ This model is a fine-tuned version of [PekingU/rtdetr_r101vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r101vd_coco_o365) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 9.5718
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+ - Map: 0.5617
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+ - Map 50: 0.6631
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+ - Map 75: 0.6137
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+ - Map Small: -1.0
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+ - Map Medium: 0.3451
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+ - Map Large: 0.5935
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+ - Mar 1: 0.6546
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+ - Mar 10: 0.7877
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+ - Mar 100: 0.8058
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+ - Mar Small: -1.0
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+ - Mar Medium: 0.5802
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+ - Mar Large: 0.8672
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+ - Map Radar (small): 0.3509
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+ - Mar 100 Radar (small): 0.8077
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+ - Map Ship management system (small): 0.6748
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+ - Mar 100 Ship management system (small): 0.8933
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+ - Map Radar (large): 0.5846
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+ - Mar 100 Radar (large): 0.8624
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+ - Map Ship management system (large): 0.7577
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+ - Mar 100 Ship management system (large): 0.9341
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+ - Map Ship management system (top): 0.789
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+ - Mar 100 Ship management system (top): 0.8356
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+ - Map Ecdis (large): 0.3281
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+ - Mar 100 Ecdis (large): 0.7652
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+ - Map Visual observation (small): 0.585
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+ - Mar 100 Visual observation (small): 0.902
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+ - Map Ecdis (small): 0.7635
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+ - Mar 100 Ecdis (small): 0.8967
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+ - Map Ship management system (table top): 0.6306
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+ - Mar 100 Ship management system (table top): 0.7882
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+ - Map Thruster control: 0.4949
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+ - Mar 100 Thruster control: 0.7447
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+ - Map Visual observation (left): 0.6062
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+ - Mar 100 Visual observation (left): 0.8395
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+ - Map Visual observation (mid): 0.7946
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+ - Mar 100 Visual observation (mid): 0.8901
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+ - Map Visual observation (right): 0.7446
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+ - Mar 100 Visual observation (right): 0.8966
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+ - Map Bow thruster: 0.2392
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+ - Mar 100 Bow thruster: 0.5167
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+ - Map Me telegraph: 0.0825
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+ - Mar 100 Me telegraph: 0.5143
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 7
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Radar (small) | Mar 100 Radar (small) | Map Ship management system (small) | Mar 100 Ship management system (small) | Map Radar (large) | Mar 100 Radar (large) | Map Ship management system (large) | Mar 100 Ship management system (large) | Map Ship management system (top) | Mar 100 Ship management system (top) | Map Ecdis (large) | Mar 100 Ecdis (large) | Map Visual observation (small) | Mar 100 Visual observation (small) | Map Ecdis (small) | Mar 100 Ecdis (small) | Map Ship management system (table top) | Mar 100 Ship management system (table top) | Map Thruster control | Mar 100 Thruster control | Map Visual observation (left) | Mar 100 Visual observation (left) | Map Visual observation (mid) | Mar 100 Visual observation (mid) | Map Visual observation (right) | Mar 100 Visual observation (right) | Map Bow thruster | Mar 100 Bow thruster | Map Me telegraph | Mar 100 Me telegraph |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------------:|:---------------------:|:----------------------------------:|:--------------------------------------:|:-----------------:|:---------------------:|:----------------------------------:|:--------------------------------------:|:--------------------------------:|:------------------------------------:|:-----------------:|:---------------------:|:------------------------------:|:----------------------------------:|:-----------------:|:---------------------:|:--------------------------------------:|:------------------------------------------:|:--------------------:|:------------------------:|:-----------------------------:|:---------------------------------:|:----------------------------:|:--------------------------------:|:------------------------------:|:----------------------------------:|:----------------:|:--------------------:|:----------------:|:--------------------:|
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+ | 14.2599 | 1.0 | 699 | 9.6242 | 0.4769 | 0.5404 | 0.5144 | -1.0 | 0.2274 | 0.5416 | 0.5866 | 0.755 | 0.7709 | -1.0 | 0.4884 | 0.8359 | 0.7408 | 0.92 | 0.672 | 0.8827 | 0.7054 | 0.9504 | 0.8329 | 0.926 | 0.7965 | 0.8692 | 0.3419 | 0.9571 | 0.2734 | 0.8627 | 0.1207 | 0.6933 | 0.4841 | 0.7059 | 0.3541 | 0.6947 | 0.5303 | 0.8961 | 0.8393 | 0.9342 | 0.2629 | 0.8466 | 0.1988 | 0.3583 | 0.0011 | 0.0667 |
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+ | 8.9356 | 2.0 | 1398 | 9.1941 | 0.5527 | 0.6652 | 0.6044 | -1.0 | 0.3212 | 0.574 | 0.6512 | 0.7882 | 0.8015 | -1.0 | 0.6608 | 0.8085 | 0.6989 | 0.8862 | 0.5273 | 0.8053 | 0.7683 | 0.9145 | 0.7209 | 0.9073 | 0.7995 | 0.8644 | 0.4929 | 0.833 | 0.4034 | 0.8392 | 0.5519 | 0.8333 | 0.6453 | 0.8618 | 0.4221 | 0.6447 | 0.5734 | 0.8474 | 0.8714 | 0.8973 | 0.412 | 0.8448 | 0.3154 | 0.5333 | 0.0874 | 0.5095 |
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+ | 8.1388 | 3.0 | 2097 | 9.7524 | 0.535 | 0.6013 | 0.5854 | -1.0 | 0.2545 | 0.574 | 0.6219 | 0.7425 | 0.7612 | -1.0 | 0.538 | 0.8183 | 0.6358 | 0.8292 | 0.5844 | 0.8013 | 0.6721 | 0.8368 | 0.7422 | 0.8829 | 0.7144 | 0.8096 | 0.4904 | 0.8562 | 0.7623 | 0.9078 | 0.5667 | 0.89 | 0.6409 | 0.7824 | 0.1853 | 0.5763 | 0.5453 | 0.7789 | 0.8362 | 0.9 | 0.5862 | 0.9207 | 0.0384 | 0.3833 | 0.0248 | 0.2619 |
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+ | 7.5951 | 4.0 | 2796 | 9.3983 | 0.5991 | 0.7001 | 0.6587 | -1.0 | 0.3745 | 0.6167 | 0.6957 | 0.8036 | 0.8188 | -1.0 | 0.6611 | 0.8746 | 0.603 | 0.8538 | 0.626 | 0.88 | 0.6211 | 0.8496 | 0.8218 | 0.9382 | 0.8062 | 0.8433 | 0.3917 | 0.8804 | 0.6202 | 0.851 | 0.8307 | 0.9433 | 0.555 | 0.8147 | 0.5143 | 0.8 | 0.6609 | 0.8579 | 0.887 | 0.9369 | 0.7174 | 0.8759 | 0.2732 | 0.5333 | 0.0579 | 0.4238 |
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+ | 7.1786 | 5.0 | 3495 | 9.1194 | 0.6117 | 0.7144 | 0.6689 | -1.0 | 0.3458 | 0.6476 | 0.6904 | 0.8136 | 0.8324 | -1.0 | 0.6649 | 0.8777 | 0.5 | 0.8538 | 0.6723 | 0.8733 | 0.7272 | 0.8795 | 0.778 | 0.9398 | 0.7803 | 0.8385 | 0.3389 | 0.8509 | 0.6484 | 0.8804 | 0.7914 | 0.9433 | 0.7053 | 0.8059 | 0.6257 | 0.8447 | 0.5945 | 0.8658 | 0.8411 | 0.9009 | 0.7812 | 0.9397 | 0.2863 | 0.5792 | 0.1053 | 0.4905 |
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+ | 7.1386 | 6.0 | 4194 | 9.9394 | 0.5353 | 0.634 | 0.5921 | -1.0 | 0.3062 | 0.5549 | 0.6429 | 0.7691 | 0.7874 | -1.0 | 0.5638 | 0.8364 | 0.3431 | 0.7631 | 0.6563 | 0.8813 | 0.5789 | 0.8393 | 0.6941 | 0.9236 | 0.721 | 0.7712 | 0.4061 | 0.8018 | 0.5685 | 0.8725 | 0.7656 | 0.91 | 0.5317 | 0.8 | 0.5194 | 0.7684 | 0.5191 | 0.8039 | 0.7994 | 0.8586 | 0.6714 | 0.8793 | 0.2223 | 0.4958 | 0.0333 | 0.4429 |
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+ | 7.0912 | 7.0 | 4893 | 9.5718 | 0.5617 | 0.6631 | 0.6137 | -1.0 | 0.3451 | 0.5935 | 0.6546 | 0.7877 | 0.8058 | -1.0 | 0.5802 | 0.8672 | 0.3509 | 0.8077 | 0.6748 | 0.8933 | 0.5846 | 0.8624 | 0.7577 | 0.9341 | 0.789 | 0.8356 | 0.3281 | 0.7652 | 0.585 | 0.902 | 0.7635 | 0.8967 | 0.6306 | 0.7882 | 0.4949 | 0.7447 | 0.6062 | 0.8395 | 0.7946 | 0.8901 | 0.7446 | 0.8966 | 0.2392 | 0.5167 | 0.0825 | 0.5143 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.5.0+cu121
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+ - Datasets 3.0.2
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+ - Tokenizers 0.20.1
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