---
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