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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: 1.4822
- Map: 0.2108
- Map 50: 0.3072
- Map 75: 0.2725
- Map Small: -1.0
- Map Medium: -1.0
- Map Large: 0.2219
- Mar 1: 0.1833
- Mar 10: 0.4195
- Mar 100: 0.736
- Mar Small: -1.0
- Mar Medium: -1.0
- Mar Large: 0.736
- Map Cashier: 0.0544
- Mar 100 Cashier: 0.8053
- Map Cx: 0.3672
- Mar 100 Cx: 0.6667
## 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 | 5 | 38.9601 | 0.0007 | 0.0042 | 0.0 | -1.0 | -1.0 | 0.001 | 0.0 | 0.0186 | 0.053 | -1.0 | -1.0 | 0.053 | 0.0004 | 0.0526 | 0.001 | 0.0533 |
| No log | 2.0 | 10 | 20.3884 | 0.0053 | 0.0378 | 0.001 | -1.0 | -1.0 | 0.0119 | 0.0 | 0.0133 | 0.11 | -1.0 | -1.0 | 0.11 | 0.0 | 0.0 | 0.0106 | 0.22 |
| No log | 3.0 | 15 | 8.6229 | 0.0257 | 0.0582 | 0.0072 | -1.0 | -1.0 | 0.0573 | 0.0333 | 0.12 | 0.1737 | -1.0 | -1.0 | 0.1737 | 0.0002 | 0.0474 | 0.0512 | 0.3 |
| No log | 4.0 | 20 | 5.4986 | 0.0061 | 0.02 | 0.0018 | -1.0 | -1.0 | 0.0175 | 0.0 | 0.0833 | 0.1 | -1.0 | -1.0 | 0.1 | 0.0 | 0.0 | 0.0122 | 0.2 |
| No log | 5.0 | 25 | 3.2810 | 0.0152 | 0.0644 | 0.0 | -1.0 | -1.0 | 0.0182 | 0.0067 | 0.0933 | 0.1989 | -1.0 | -1.0 | 0.1989 | 0.004 | 0.1579 | 0.0264 | 0.24 |
| No log | 6.0 | 30 | 2.4527 | 0.0174 | 0.0707 | 0.0008 | -1.0 | -1.0 | 0.0201 | 0.0067 | 0.0667 | 0.3286 | -1.0 | -1.0 | 0.3286 | 0.0076 | 0.3105 | 0.0272 | 0.3467 |
| No log | 7.0 | 35 | 2.1059 | 0.0222 | 0.0715 | 0.0077 | -1.0 | 0.0 | 0.0244 | 0.0233 | 0.0993 | 0.4733 | -1.0 | 0.0 | 0.4857 | 0.017 | 0.6 | 0.0273 | 0.3467 |
| No log | 8.0 | 40 | 2.1013 | 0.035 | 0.117 | 0.0054 | -1.0 | -1.0 | 0.0354 | 0.0333 | 0.0467 | 0.3653 | -1.0 | -1.0 | 0.3653 | 0.0204 | 0.6105 | 0.0497 | 0.12 |
| No log | 9.0 | 45 | 1.9401 | 0.0412 | 0.1081 | 0.0058 | -1.0 | -1.0 | 0.042 | 0.0267 | 0.04 | 0.3639 | -1.0 | -1.0 | 0.3639 | 0.0184 | 0.6211 | 0.064 | 0.1067 |
| No log | 10.0 | 50 | 2.2227 | 0.0071 | 0.0254 | 0.0017 | -1.0 | -1.0 | 0.0075 | 0.0 | 0.0433 | 0.256 | -1.0 | -1.0 | 0.256 | 0.0044 | 0.3053 | 0.0098 | 0.2067 |
| No log | 11.0 | 55 | 1.9404 | 0.0126 | 0.033 | 0.01 | -1.0 | -1.0 | 0.0127 | 0.0 | 0.08 | 0.333 | -1.0 | -1.0 | 0.333 | 0.0082 | 0.4526 | 0.0169 | 0.2133 |
| No log | 12.0 | 60 | 1.8410 | 0.0186 | 0.0658 | 0.0078 | -1.0 | -1.0 | 0.0187 | 0.0 | 0.0733 | 0.3591 | -1.0 | -1.0 | 0.3591 | 0.0125 | 0.5316 | 0.0247 | 0.1867 |
| No log | 13.0 | 65 | 1.7476 | 0.0735 | 0.1829 | 0.0136 | -1.0 | -1.0 | 0.0736 | 0.0633 | 0.1133 | 0.4335 | -1.0 | -1.0 | 0.4335 | 0.0105 | 0.4737 | 0.1366 | 0.3933 |
| No log | 14.0 | 70 | 1.8939 | 0.0634 | 0.1492 | 0.0276 | -1.0 | -1.0 | 0.0638 | 0.05 | 0.2033 | 0.3719 | -1.0 | -1.0 | 0.3719 | 0.0032 | 0.2105 | 0.1236 | 0.5333 |
| No log | 15.0 | 75 | 1.7653 | 0.0535 | 0.1438 | 0.0387 | -1.0 | -1.0 | 0.0554 | 0.0964 | 0.1929 | 0.4917 | -1.0 | -1.0 | 0.4917 | 0.0103 | 0.4263 | 0.0968 | 0.5571 |
| No log | 16.0 | 80 | 1.6493 | 0.0988 | 0.2205 | 0.0787 | -1.0 | -1.0 | 0.1014 | 0.0733 | 0.2419 | 0.6505 | -1.0 | -1.0 | 0.6505 | 0.0225 | 0.7211 | 0.1751 | 0.58 |
| No log | 17.0 | 85 | 1.7624 | 0.1198 | 0.2464 | 0.0858 | 0.0 | -1.0 | 0.1289 | 0.1167 | 0.2333 | 0.6149 | 0.0 | -1.0 | 0.6352 | 0.0285 | 0.6632 | 0.211 | 0.5667 |
| No log | 18.0 | 90 | 1.8609 | 0.149 | 0.3181 | 0.0874 | -1.0 | -1.0 | 0.1517 | 0.13 | 0.2861 | 0.5789 | -1.0 | -1.0 | 0.5789 | 0.0403 | 0.6579 | 0.2576 | 0.5 |
| No log | 19.0 | 95 | 1.6860 | 0.1455 | 0.257 | 0.1026 | -1.0 | -1.0 | 0.1497 | 0.1393 | 0.2697 | 0.6496 | -1.0 | -1.0 | 0.6496 | 0.039 | 0.7421 | 0.2521 | 0.5571 |
| No log | 20.0 | 100 | 1.7541 | 0.1966 | 0.3274 | 0.2313 | -1.0 | -1.0 | 0.1994 | 0.17 | 0.317 | 0.6182 | -1.0 | -1.0 | 0.6182 | 0.0379 | 0.6632 | 0.3553 | 0.5733 |
| No log | 21.0 | 105 | 1.7043 | 0.1735 | 0.2572 | 0.1786 | -1.0 | 0.0 | 0.1913 | 0.1821 | 0.3295 | 0.6252 | -1.0 | 0.0 | 0.6472 | 0.0459 | 0.6789 | 0.3012 | 0.5714 |
| No log | 22.0 | 110 | 1.5489 | 0.1959 | 0.3051 | 0.264 | -1.0 | -1.0 | 0.2039 | 0.17 | 0.3568 | 0.6896 | -1.0 | -1.0 | 0.6896 | 0.046 | 0.7526 | 0.3458 | 0.6267 |
| No log | 23.0 | 115 | 1.6402 | 0.166 | 0.293 | 0.1791 | -1.0 | -1.0 | 0.1773 | 0.1432 | 0.2851 | 0.6496 | -1.0 | -1.0 | 0.6496 | 0.048 | 0.7526 | 0.284 | 0.5467 |
| No log | 24.0 | 120 | 1.5800 | 0.188 | 0.3099 | 0.1815 | -1.0 | -1.0 | 0.1965 | 0.1791 | 0.3337 | 0.693 | -1.0 | -1.0 | 0.693 | 0.0408 | 0.7526 | 0.3352 | 0.6333 |
| No log | 25.0 | 125 | 1.5566 | 0.1921 | 0.3038 | 0.2227 | -1.0 | -1.0 | 0.2037 | 0.17 | 0.4244 | 0.6928 | -1.0 | -1.0 | 0.6928 | 0.0642 | 0.7789 | 0.32 | 0.6067 |
| No log | 26.0 | 130 | 1.7227 | 0.2044 | 0.3337 | 0.2155 | -1.0 | -1.0 | 0.2145 | 0.1667 | 0.3319 | 0.5968 | -1.0 | -1.0 | 0.5968 | 0.0484 | 0.6737 | 0.3604 | 0.52 |
| No log | 27.0 | 135 | 1.5184 | 0.2095 | 0.3161 | 0.2389 | -1.0 | -1.0 | 0.2211 | 0.1877 | 0.3163 | 0.7026 | -1.0 | -1.0 | 0.7026 | 0.0603 | 0.8053 | 0.3586 | 0.6 |
| No log | 28.0 | 140 | 1.5156 | 0.2172 | 0.3273 | 0.2672 | -1.0 | -1.0 | 0.2286 | 0.2077 | 0.4402 | 0.7226 | -1.0 | -1.0 | 0.7226 | 0.0681 | 0.8053 | 0.3664 | 0.64 |
| No log | 29.0 | 145 | 1.6211 | 0.1652 | 0.2652 | 0.2007 | -1.0 | -1.0 | 0.1737 | 0.15 | 0.4081 | 0.677 | -1.0 | -1.0 | 0.677 | 0.0463 | 0.7474 | 0.2841 | 0.6067 |
| No log | 30.0 | 150 | 1.4822 | 0.2108 | 0.3072 | 0.2725 | -1.0 | -1.0 | 0.2219 | 0.1833 | 0.4195 | 0.736 | -1.0 | -1.0 | 0.736 | 0.0544 | 0.8053 | 0.3672 | 0.6667 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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