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---
base_model: microsoft/conditional-detr-resnet-50
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: queue_detection
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# queue_detection
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2068
- Map: 0.9189
- Map 50: 0.9898
- Map 75: 0.9755
- Map Small: -1.0
- Map Medium: 0.1871
- Map Large: 0.9234
- Mar 1: 0.49
- Mar 10: 0.9447
- Mar 100: 0.9466
- Mar Small: -1.0
- Mar Medium: 0.2615
- Mar Large: 0.9505
- Map Cashier: 0.935
- Mar 100 Cashier: 0.9592
- Map Cx: 0.9027
- Mar 100 Cx: 0.9341
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| 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 Cashier | Mar 100 Cashier | Map Cx | Mar 100 Cx |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:|:------:|:----------:|
| No log | 1.0 | 218 | 1.3811 | 0.2363 | 0.4099 | 0.2515 | -1.0 | 0.0 | 0.2376 | 0.2364 | 0.5459 | 0.719 | -1.0 | 0.0 | 0.7238 | 0.2986 | 0.7856 | 0.174 | 0.6523 |
| No log | 2.0 | 436 | 0.9194 | 0.575 | 0.8212 | 0.6661 | -1.0 | 0.038 | 0.5796 | 0.3518 | 0.7412 | 0.7921 | -1.0 | 0.1 | 0.7978 | 0.6716 | 0.8368 | 0.4783 | 0.7473 |
| 2.4678 | 3.0 | 654 | 0.6968 | 0.6863 | 0.9358 | 0.8313 | -1.0 | 0.0679 | 0.6906 | 0.3901 | 0.7636 | 0.7806 | -1.0 | 0.12 | 0.7848 | 0.7373 | 0.8132 | 0.6354 | 0.7481 |
| 2.4678 | 4.0 | 872 | 0.6535 | 0.7043 | 0.9566 | 0.8452 | -1.0 | 0.0421 | 0.7091 | 0.3916 | 0.7748 | 0.7871 | -1.0 | 0.1615 | 0.7909 | 0.772 | 0.8416 | 0.6366 | 0.7326 |
| 0.723 | 5.0 | 1090 | 0.5906 | 0.7447 | 0.9667 | 0.8788 | -1.0 | 0.0367 | 0.7494 | 0.4113 | 0.8026 | 0.8122 | -1.0 | 0.0955 | 0.8166 | 0.7913 | 0.8551 | 0.6981 | 0.7693 |
| 0.723 | 6.0 | 1308 | 0.5508 | 0.7473 | 0.9631 | 0.893 | 0.0 | 0.0672 | 0.7527 | 0.4199 | 0.8021 | 0.8067 | 0.0 | 0.1077 | 0.812 | 0.7943 | 0.8428 | 0.7002 | 0.7705 |
| 0.5955 | 7.0 | 1526 | 0.5073 | 0.7626 | 0.9656 | 0.9062 | -1.0 | 0.0515 | 0.7673 | 0.423 | 0.8156 | 0.8182 | -1.0 | 0.2273 | 0.8226 | 0.7917 | 0.8476 | 0.7335 | 0.7887 |
| 0.5955 | 8.0 | 1744 | 0.5195 | 0.7452 | 0.9721 | 0.8832 | -1.0 | 0.0559 | 0.7511 | 0.417 | 0.8022 | 0.8105 | -1.0 | 0.1607 | 0.8149 | 0.7925 | 0.8448 | 0.6979 | 0.7762 |
| 0.5955 | 9.0 | 1962 | 0.4328 | 0.8028 | 0.9786 | 0.938 | -1.0 | 0.0481 | 0.808 | 0.441 | 0.8515 | 0.8573 | -1.0 | 0.1154 | 0.8625 | 0.8348 | 0.8846 | 0.7708 | 0.8299 |
| 0.5055 | 10.0 | 2180 | 0.4195 | 0.8085 | 0.9847 | 0.944 | 0.0 | 0.0616 | 0.8129 | 0.4365 | 0.8526 | 0.8591 | 0.0 | 0.1111 | 0.8635 | 0.8241 | 0.8793 | 0.7929 | 0.839 |
| 0.5055 | 11.0 | 2398 | 0.4155 | 0.8145 | 0.9803 | 0.9465 | 0.0 | 0.0555 | 0.8236 | 0.4455 | 0.8556 | 0.8598 | 0.0 | 0.1 | 0.8691 | 0.8373 | 0.8813 | 0.7918 | 0.8383 |
| 0.457 | 12.0 | 2616 | 0.3886 | 0.8301 | 0.984 | 0.9549 | 0.0 | 0.1278 | 0.8355 | 0.4513 | 0.8691 | 0.8731 | 0.0 | 0.1625 | 0.878 | 0.8596 | 0.9024 | 0.8006 | 0.8438 |
| 0.457 | 13.0 | 2834 | 0.3750 | 0.829 | 0.9878 | 0.9563 | -1.0 | 0.1024 | 0.8333 | 0.4508 | 0.8692 | 0.8735 | -1.0 | 0.17 | 0.8772 | 0.8507 | 0.894 | 0.8073 | 0.8531 |
| 0.3971 | 14.0 | 3052 | 0.3571 | 0.8427 | 0.9871 | 0.9524 | -1.0 | 0.0895 | 0.849 | 0.4576 | 0.8803 | 0.8843 | -1.0 | 0.1531 | 0.8903 | 0.8627 | 0.9071 | 0.8227 | 0.8615 |
| 0.3971 | 15.0 | 3270 | 0.3637 | 0.8398 | 0.983 | 0.9587 | 0.0 | 0.0682 | 0.8481 | 0.4565 | 0.8754 | 0.8779 | 0.0 | 0.0972 | 0.8867 | 0.8692 | 0.9062 | 0.8104 | 0.8496 |
| 0.3971 | 16.0 | 3488 | 0.3373 | 0.8494 | 0.9894 | 0.9514 | -1.0 | 0.2611 | 0.8534 | 0.4653 | 0.8849 | 0.8883 | -1.0 | 0.3929 | 0.8923 | 0.8756 | 0.9121 | 0.8233 | 0.8645 |
| 0.387 | 17.0 | 3706 | 0.2885 | 0.8744 | 0.9895 | 0.9712 | -1.0 | 0.1702 | 0.8786 | 0.472 | 0.9076 | 0.9083 | -1.0 | 0.2269 | 0.9124 | 0.8951 | 0.927 | 0.8537 | 0.8897 |
| 0.387 | 18.0 | 3924 | 0.2835 | 0.8737 | 0.9893 | 0.9742 | -1.0 | 0.1473 | 0.8773 | 0.4732 | 0.9064 | 0.9085 | -1.0 | 0.1833 | 0.9119 | 0.8977 | 0.9287 | 0.8498 | 0.8883 |
| 0.3216 | 19.0 | 4142 | 0.2699 | 0.8816 | 0.9894 | 0.9722 | -1.0 | 0.1445 | 0.8865 | 0.4786 | 0.9123 | 0.9142 | -1.0 | 0.3286 | 0.9192 | 0.9065 | 0.9364 | 0.8568 | 0.8919 |
| 0.3216 | 20.0 | 4360 | 0.2711 | 0.8813 | 0.9895 | 0.9727 | -1.0 | 0.1181 | 0.8858 | 0.4775 | 0.914 | 0.9156 | -1.0 | 0.1917 | 0.9199 | 0.9041 | 0.9348 | 0.8584 | 0.8963 |
| 0.3117 | 21.0 | 4578 | 0.2489 | 0.8889 | 0.9897 | 0.9763 | -1.0 | 0.2022 | 0.8934 | 0.4817 | 0.9215 | 0.9228 | -1.0 | 0.25 | 0.9261 | 0.911 | 0.9415 | 0.8668 | 0.9041 |
| 0.3117 | 22.0 | 4796 | 0.2739 | 0.8854 | 0.9846 | 0.9685 | 0.0 | 0.1351 | 0.8944 | 0.4797 | 0.9132 | 0.9159 | 0.0 | 0.2059 | 0.9244 | 0.9071 | 0.9351 | 0.8638 | 0.8968 |
| 0.2812 | 23.0 | 5014 | 0.2489 | 0.8989 | 0.9895 | 0.9702 | -1.0 | 0.188 | 0.9023 | 0.482 | 0.9246 | 0.9269 | -1.0 | 0.2536 | 0.9309 | 0.9183 | 0.9419 | 0.8795 | 0.9118 |
| 0.2812 | 24.0 | 5232 | 0.2267 | 0.9059 | 0.9897 | 0.9829 | -1.0 | 0.2171 | 0.9077 | 0.4861 | 0.9328 | 0.9341 | -1.0 | 0.2818 | 0.9371 | 0.9253 | 0.9488 | 0.8864 | 0.9193 |
| 0.2812 | 25.0 | 5450 | 0.2326 | 0.9057 | 0.9899 | 0.9758 | -1.0 | 0.1995 | 0.9097 | 0.4837 | 0.9316 | 0.9326 | -1.0 | 0.2286 | 0.9371 | 0.9196 | 0.9449 | 0.8917 | 0.9202 |
| 0.26 | 26.0 | 5668 | 0.2111 | 0.915 | 0.9899 | 0.9792 | 0.0 | 0.2832 | 0.9199 | 0.4902 | 0.9408 | 0.9423 | 0.0 | 0.415 | 0.9457 | 0.9267 | 0.9521 | 0.9032 | 0.9325 |
| 0.26 | 27.0 | 5886 | 0.2184 | 0.9132 | 0.9898 | 0.9764 | -1.0 | 0.2546 | 0.9167 | 0.4872 | 0.9375 | 0.9389 | -1.0 | 0.2767 | 0.943 | 0.9277 | 0.95 | 0.8988 | 0.9279 |
| 0.2498 | 28.0 | 6104 | 0.2200 | 0.9159 | 0.9892 | 0.9756 | -1.0 | 0.1921 | 0.9197 | 0.4872 | 0.9397 | 0.9413 | -1.0 | 0.275 | 0.9446 | 0.9314 | 0.9547 | 0.9004 | 0.9279 |
| 0.2498 | 29.0 | 6322 | 0.2075 | 0.9209 | 0.9899 | 0.9791 | -1.0 | 0.317 | 0.9241 | 0.4904 | 0.9448 | 0.9454 | -1.0 | 0.34 | 0.9475 | 0.9345 | 0.9565 | 0.9073 | 0.9342 |
| 0.2519 | 30.0 | 6540 | 0.2068 | 0.9189 | 0.9898 | 0.9755 | -1.0 | 0.1871 | 0.9234 | 0.49 | 0.9447 | 0.9466 | -1.0 | 0.2615 | 0.9505 | 0.935 | 0.9592 | 0.9027 | 0.9341 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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