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---
library_name: transformers
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: conditional-detr-resnet-50-uLED-obj-detect-test
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. -->
# conditional-detr-resnet-50-uLED-obj-detect-test
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0912
- Map: 0.9334
- Map 50: 0.9684
- Map 75: 0.9684
- Map Small: -1.0
- Map Medium: 0.9334
- Map Large: -1.0
- Mar 1: 0.0125
- Mar 10: 0.1259
- Mar 100: 0.9777
- Mar Small: -1.0
- Mar Medium: 0.9777
- Mar Large: -1.0
- Map Uled: 0.9334
- Mar 100 Uled: 0.9777
- Map Trash: -1.0
- Mar 100 Trash: -1.0
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 30
### 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 Uled | Mar 100 Uled | Map Trash | Mar 100 Trash |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------:|:------------:|:---------:|:-------------:|
| No log | 1.0 | 41 | 0.2460 | 0.7925 | 0.9619 | 0.9382 | -1.0 | 0.7925 | -1.0 | 0.0115 | 0.1133 | 0.8652 | -1.0 | 0.8652 | -1.0 | 0.7925 | 0.8652 | -1.0 | -1.0 |
| No log | 2.0 | 82 | 0.2123 | 0.8121 | 0.9671 | 0.9527 | -1.0 | 0.8121 | -1.0 | 0.0111 | 0.1125 | 0.8797 | -1.0 | 0.8797 | -1.0 | 0.8121 | 0.8797 | -1.0 | -1.0 |
| No log | 3.0 | 123 | 0.1597 | 0.8576 | 0.9645 | 0.963 | -1.0 | 0.8576 | -1.0 | 0.0118 | 0.1181 | 0.9217 | -1.0 | 0.9217 | -1.0 | 0.8576 | 0.9217 | -1.0 | -1.0 |
| No log | 4.0 | 164 | 0.1645 | 0.8532 | 0.9644 | 0.9606 | -1.0 | 0.8532 | -1.0 | 0.0118 | 0.1184 | 0.9174 | -1.0 | 0.9174 | -1.0 | 0.8532 | 0.9174 | -1.0 | -1.0 |
| No log | 5.0 | 205 | 0.2037 | 0.824 | 0.9632 | 0.9614 | -1.0 | 0.824 | -1.0 | 0.0115 | 0.1142 | 0.8826 | -1.0 | 0.8826 | -1.0 | 0.824 | 0.8826 | -1.0 | -1.0 |
| No log | 6.0 | 246 | 0.1342 | 0.8864 | 0.9672 | 0.9665 | -1.0 | 0.8864 | -1.0 | 0.0119 | 0.1213 | 0.9429 | -1.0 | 0.9429 | -1.0 | 0.8864 | 0.9429 | -1.0 | -1.0 |
| No log | 7.0 | 287 | 0.1365 | 0.8821 | 0.9677 | 0.9672 | -1.0 | 0.8821 | -1.0 | 0.0121 | 0.1218 | 0.9362 | -1.0 | 0.9362 | -1.0 | 0.8821 | 0.9362 | -1.0 | -1.0 |
| No log | 8.0 | 328 | 0.1470 | 0.872 | 0.9666 | 0.9662 | -1.0 | 0.872 | -1.0 | 0.0119 | 0.12 | 0.9326 | -1.0 | 0.9326 | -1.0 | 0.872 | 0.9326 | -1.0 | -1.0 |
| No log | 9.0 | 369 | 0.1783 | 0.8495 | 0.9678 | 0.9673 | -1.0 | 0.8495 | -1.0 | 0.0118 | 0.118 | 0.9017 | -1.0 | 0.9017 | -1.0 | 0.8495 | 0.9017 | -1.0 | -1.0 |
| No log | 10.0 | 410 | 0.1563 | 0.8676 | 0.9662 | 0.9643 | -1.0 | 0.8676 | -1.0 | 0.012 | 0.1203 | 0.9225 | -1.0 | 0.9225 | -1.0 | 0.8676 | 0.9225 | -1.0 | -1.0 |
| No log | 11.0 | 451 | 0.1458 | 0.8783 | 0.966 | 0.9658 | -1.0 | 0.8783 | -1.0 | 0.012 | 0.121 | 0.9321 | -1.0 | 0.9321 | -1.0 | 0.8783 | 0.9321 | -1.0 | -1.0 |
| No log | 12.0 | 492 | 0.1273 | 0.8939 | 0.9669 | 0.9667 | -1.0 | 0.8939 | -1.0 | 0.0123 | 0.1234 | 0.9462 | -1.0 | 0.9462 | -1.0 | 0.8939 | 0.9462 | -1.0 | -1.0 |
| 0.2348 | 13.0 | 533 | 0.1376 | 0.8862 | 0.9683 | 0.968 | -1.0 | 0.8862 | -1.0 | 0.0121 | 0.1217 | 0.9404 | -1.0 | 0.9404 | -1.0 | 0.8862 | 0.9404 | -1.0 | -1.0 |
| 0.2348 | 14.0 | 574 | 0.1338 | 0.8865 | 0.9669 | 0.9668 | -1.0 | 0.8865 | -1.0 | 0.0122 | 0.1222 | 0.9422 | -1.0 | 0.9422 | -1.0 | 0.8865 | 0.9422 | -1.0 | -1.0 |
| 0.2348 | 15.0 | 615 | 0.1258 | 0.8917 | 0.9685 | 0.9685 | -1.0 | 0.8917 | -1.0 | 0.012 | 0.1221 | 0.9454 | -1.0 | 0.9454 | -1.0 | 0.8917 | 0.9454 | -1.0 | -1.0 |
| 0.2348 | 16.0 | 656 | 0.1206 | 0.8998 | 0.9689 | 0.9689 | -1.0 | 0.8998 | -1.0 | 0.0123 | 0.1233 | 0.9524 | -1.0 | 0.9524 | -1.0 | 0.8998 | 0.9524 | -1.0 | -1.0 |
| 0.2348 | 17.0 | 697 | 0.1075 | 0.911 | 0.969 | 0.969 | -1.0 | 0.911 | -1.0 | 0.0123 | 0.1238 | 0.9612 | -1.0 | 0.9612 | -1.0 | 0.911 | 0.9612 | -1.0 | -1.0 |
| 0.2348 | 18.0 | 738 | 0.1084 | 0.9113 | 0.9692 | 0.9691 | -1.0 | 0.9113 | -1.0 | 0.0123 | 0.1237 | 0.9628 | -1.0 | 0.9628 | -1.0 | 0.9113 | 0.9628 | -1.0 | -1.0 |
| 0.2348 | 19.0 | 779 | 0.1104 | 0.91 | 0.9688 | 0.9688 | -1.0 | 0.91 | -1.0 | 0.0123 | 0.1236 | 0.9602 | -1.0 | 0.9602 | -1.0 | 0.91 | 0.9602 | -1.0 | -1.0 |
| 0.2348 | 20.0 | 820 | 0.1097 | 0.9103 | 0.9693 | 0.9693 | -1.0 | 0.9103 | -1.0 | 0.0123 | 0.1241 | 0.9616 | -1.0 | 0.9616 | -1.0 | 0.9103 | 0.9616 | -1.0 | -1.0 |
| 0.2348 | 21.0 | 861 | 0.1111 | 0.9106 | 0.9666 | 0.9665 | -1.0 | 0.9106 | -1.0 | 0.0123 | 0.1242 | 0.9624 | -1.0 | 0.9624 | -1.0 | 0.9106 | 0.9624 | -1.0 | -1.0 |
| 0.2348 | 22.0 | 902 | 0.1007 | 0.923 | 0.9667 | 0.9666 | -1.0 | 0.923 | -1.0 | 0.0125 | 0.1251 | 0.972 | -1.0 | 0.972 | -1.0 | 0.923 | 0.972 | -1.0 | -1.0 |
| 0.2348 | 23.0 | 943 | 0.1080 | 0.9103 | 0.9671 | 0.9671 | -1.0 | 0.9103 | -1.0 | 0.0123 | 0.1242 | 0.9612 | -1.0 | 0.9612 | -1.0 | 0.9103 | 0.9612 | -1.0 | -1.0 |
| 0.2348 | 24.0 | 984 | 0.0987 | 0.9197 | 0.967 | 0.967 | -1.0 | 0.9197 | -1.0 | 0.0124 | 0.1253 | 0.9697 | -1.0 | 0.9697 | -1.0 | 0.9197 | 0.9697 | -1.0 | -1.0 |
| 0.1648 | 25.0 | 1025 | 0.0979 | 0.9226 | 0.9675 | 0.9675 | -1.0 | 0.9226 | -1.0 | 0.0125 | 0.1253 | 0.9715 | -1.0 | 0.9715 | -1.0 | 0.9226 | 0.9715 | -1.0 | -1.0 |
| 0.1648 | 26.0 | 1066 | 0.0912 | 0.9334 | 0.9684 | 0.9684 | -1.0 | 0.9334 | -1.0 | 0.0125 | 0.1259 | 0.9777 | -1.0 | 0.9777 | -1.0 | 0.9334 | 0.9777 | -1.0 | -1.0 |
| 0.1648 | 27.0 | 1107 | 0.0926 | 0.9311 | 0.9682 | 0.9682 | -1.0 | 0.9311 | -1.0 | 0.0125 | 0.1258 | 0.9763 | -1.0 | 0.9763 | -1.0 | 0.9311 | 0.9763 | -1.0 | -1.0 |
| 0.1648 | 28.0 | 1148 | 0.0933 | 0.9301 | 0.9682 | 0.9681 | -1.0 | 0.9301 | -1.0 | 0.0125 | 0.1258 | 0.9756 | -1.0 | 0.9756 | -1.0 | 0.9301 | 0.9756 | -1.0 | -1.0 |
| 0.1648 | 29.0 | 1189 | 0.0937 | 0.9301 | 0.9682 | 0.9681 | -1.0 | 0.9301 | -1.0 | 0.0125 | 0.1259 | 0.9758 | -1.0 | 0.9758 | -1.0 | 0.9301 | 0.9758 | -1.0 | -1.0 |
| 0.1648 | 30.0 | 1230 | 0.0932 | 0.9311 | 0.9682 | 0.9681 | -1.0 | 0.9311 | -1.0 | 0.0125 | 0.126 | 0.9763 | -1.0 | 0.9763 | -1.0 | 0.9311 | 0.9763 | -1.0 | -1.0 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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