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---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- object-detection
- vision
- generated_from_trainer
model-index:
- name: detr-finetuned-cppe-5-10k-steps
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. -->
# detr-finetuned-cppe-5-10k-steps
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6238
- Map: 0.1619
- Map 50: 0.3226
- Map 75: 0.1429
- Map Small: 0.0501
- Map Medium: 0.129
- Map Large: 0.227
- Mar 1: 0.1773
- Mar 10: 0.3167
- Mar 100: 0.3392
- Mar Small: 0.128
- Mar Medium: 0.2626
- Mar Large: 0.4711
- Map Coverall: 0.4278
- Mar 100 Coverall: 0.6532
- Map Face Shield: 0.1078
- Mar 100 Face Shield: 0.2937
- Map Gloves: 0.0679
- Mar 100 Gloves: 0.2991
- Map Goggles: 0.0102
- Mar 100 Goggles: 0.0985
- Map Mask: 0.1959
- Mar 100 Mask: 0.3516
## 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: 4
- eval_batch_size: 8
- seed: 1337
- 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: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Coverall | Map Face Shield | Map Gloves | Map Goggles | Map Mask | Map Large | Map Medium | Map Small | Mar 1 | Mar 10 | Mar 100 | Mar 100 Coverall | Mar 100 Face Shield | Mar 100 Gloves | Mar 100 Goggles | Mar 100 Mask | Mar Large | Mar Medium | Mar Small |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------------:|:---------------:|:----------:|:-----------:|:--------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:----------------:|:-------------------:|:--------------:|:---------------:|:------------:|:---------:|:----------:|:---------:|
| 2.5499 | 1.0 | 213 | 2.3248 | 0.0367 | 0.0798 | 0.0325 | 0.1697 | 0.0 | 0.0078 | 0.0 | 0.0058 | 0.0417 | 0.0114 | 0.0018 | 0.0555 | 0.1278 | 0.1648 | 0.5104 | 0.0 | 0.1813 | 0.0 | 0.1324 | 0.2026 | 0.1044 | 0.0416 |
| 2.1119 | 2.0 | 426 | 2.0867 | 0.0493 | 0.1064 | 0.0379 | 0.1978 | 0.0 | 0.0253 | 0.0 | 0.0236 | 0.0574 | 0.0304 | 0.0072 | 0.0805 | 0.1667 | 0.2069 | 0.5874 | 0.0 | 0.204 | 0.0 | 0.2431 | 0.2325 | 0.1572 | 0.073 |
| 2.0052 | 3.0 | 639 | 2.1689 | 0.0563 | 0.1279 | 0.0441 | 0.0154 | 0.0414 | 0.0688 | 0.0818 | 0.1611 | 0.1793 | 0.0616 | 0.131 | 0.2199 | 0.2039 | 0.4757 | 0.0 | 0.0 | 0.0158 | 0.1942 | 0.0 | 0.0 | 0.0618 | 0.2267 |
| 1.9373 | 4.0 | 852 | 1.9264 | 0.0813 | 0.1755 | 0.0679 | 0.0125 | 0.056 | 0.0953 | 0.0916 | 0.1816 | 0.206 | 0.0662 | 0.1446 | 0.2549 | 0.3464 | 0.6302 | 0.0 | 0.0 | 0.0245 | 0.2004 | 0.0 | 0.0 | 0.0357 | 0.1996 |
| 1.8396 | 5.0 | 1065 | 1.8418 | 0.0958 | 0.208 | 0.0731 | 0.0194 | 0.0751 | 0.1163 | 0.116 | 0.2195 | 0.2399 | 0.095 | 0.1802 | 0.2939 | 0.3278 | 0.6054 | 0.0228 | 0.1127 | 0.031 | 0.2071 | 0.0 | 0.0 | 0.0975 | 0.2742 |
| 1.7659 | 6.0 | 1278 | 1.8737 | 0.1004 | 0.2399 | 0.0791 | 0.0377 | 0.0913 | 0.1207 | 0.1226 | 0.2204 | 0.2382 | 0.0808 | 0.1912 | 0.295 | 0.2906 | 0.5856 | 0.0439 | 0.1165 | 0.0497 | 0.2281 | 0.0 | 0.0 | 0.1178 | 0.2609 |
| 1.6415 | 7.0 | 1491 | 1.7200 | 0.1305 | 0.2822 | 0.105 | 0.044 | 0.1026 | 0.172 | 0.1505 | 0.2689 | 0.2891 | 0.1047 | 0.2239 | 0.386 | 0.3916 | 0.6257 | 0.0516 | 0.1962 | 0.0526 | 0.2554 | 0.0059 | 0.0523 | 0.1508 | 0.316 |
| 1.6405 | 8.0 | 1704 | 1.6820 | 0.1411 | 0.2866 | 0.1303 | 0.0542 | 0.1162 | 0.1854 | 0.1572 | 0.2867 | 0.3088 | 0.1204 | 0.2432 | 0.4042 | 0.4147 | 0.6347 | 0.0549 | 0.2468 | 0.0592 | 0.271 | 0.0053 | 0.0631 | 0.1713 | 0.3284 |
| 1.5513 | 9.0 | 1917 | 1.6380 | 0.1546 | 0.3144 | 0.1307 | 0.0569 | 0.1215 | 0.2131 | 0.17 | 0.3014 | 0.323 | 0.112 | 0.2563 | 0.4431 | 0.4352 | 0.6414 | 0.0804 | 0.2544 | 0.0657 | 0.2871 | 0.0062 | 0.0846 | 0.1853 | 0.3476 |
| 1.5564 | 10.0 | 2130 | 1.6238 | 0.1619 | 0.3226 | 0.1429 | 0.0501 | 0.129 | 0.227 | 0.1773 | 0.3167 | 0.3392 | 0.128 | 0.2626 | 0.4711 | 0.4278 | 0.6532 | 0.1078 | 0.2937 | 0.0679 | 0.2991 | 0.0102 | 0.0985 | 0.1959 | 0.3516 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
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