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---
base_model: PekingU/rtdetr_r50vd_coco_o365
tags:
- generated_from_trainer
model-index:
- name: rtdetr-r50-cppe5-finetune
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. -->
# rtdetr-r50-cppe5-finetune
This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co./PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 9.7524
- Map: 0.5298
- Map 50: 0.7903
- Map 75: 0.5632
- Map Small: 0.5092
- Map Medium: 0.4212
- Map Large: 0.6655
- Mar 1: 0.4001
- Mar 10: 0.6526
- Mar 100: 0.711
- Mar Small: 0.6038
- Mar Medium: 0.5835
- Mar Large: 0.8378
- Map Coverall: 0.6271
- Mar 100 Coverall: 0.8308
- Map Face Shield: 0.4839
- Mar 100 Face Shield: 0.7706
- Map Gloves: 0.5775
- Mar 100 Gloves: 0.6492
- Map Goggles: 0.425
- Mar 100 Goggles: 0.6103
- Map Mask: 0.5354
- Mar 100 Mask: 0.6941
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
### 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 Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| No log | 1.0 | 107 | 216.6647 | 0.0037 | 0.0089 | 0.0022 | 0.0032 | 0.0183 | 0.014 | 0.0242 | 0.1046 | 0.1966 | 0.0405 | 0.1831 | 0.4092 | 0.0056 | 0.2649 | 0.001 | 0.1962 | 0.0021 | 0.0719 | 0.0008 | 0.2215 | 0.0091 | 0.2284 |
| No log | 2.0 | 214 | 96.4364 | 0.0294 | 0.0559 | 0.0257 | 0.0169 | 0.0297 | 0.0299 | 0.0707 | 0.1835 | 0.298 | 0.0948 | 0.2203 | 0.4591 | 0.0888 | 0.5527 | 0.001 | 0.3203 | 0.021 | 0.1259 | 0.0014 | 0.2154 | 0.0346 | 0.2756 |
| No log | 3.0 | 321 | 28.5504 | 0.1576 | 0.294 | 0.1448 | 0.0752 | 0.0925 | 0.2629 | 0.1621 | 0.3534 | 0.4661 | 0.347 | 0.3964 | 0.6546 | 0.4399 | 0.6518 | 0.0021 | 0.3797 | 0.1282 | 0.3866 | 0.0045 | 0.4 | 0.2132 | 0.5124 |
| No log | 4.0 | 428 | 17.1997 | 0.2324 | 0.408 | 0.2295 | 0.1228 | 0.1816 | 0.3288 | 0.2317 | 0.4133 | 0.5 | 0.3527 | 0.4438 | 0.6543 | 0.5101 | 0.6396 | 0.0093 | 0.4671 | 0.1827 | 0.4513 | 0.1553 | 0.4062 | 0.3045 | 0.5356 |
| 117.1144 | 5.0 | 535 | 14.8812 | 0.2495 | 0.4498 | 0.2479 | 0.1261 | 0.1962 | 0.4086 | 0.253 | 0.4388 | 0.5189 | 0.3485 | 0.4683 | 0.7111 | 0.5078 | 0.6752 | 0.0291 | 0.5013 | 0.2265 | 0.4491 | 0.1715 | 0.4246 | 0.3129 | 0.5444 |
| 117.1144 | 6.0 | 642 | 13.5348 | 0.2572 | 0.4698 | 0.2541 | 0.1377 | 0.1905 | 0.424 | 0.2532 | 0.4315 | 0.4895 | 0.314 | 0.4481 | 0.6649 | 0.5166 | 0.6716 | 0.026 | 0.4873 | 0.2391 | 0.3754 | 0.1866 | 0.3754 | 0.3178 | 0.5378 |
| 117.1144 | 7.0 | 749 | 12.7545 | 0.2812 | 0.5035 | 0.2612 | 0.1618 | 0.2143 | 0.4653 | 0.2595 | 0.4568 | 0.496 | 0.3394 | 0.4438 | 0.6648 | 0.5152 | 0.6815 | 0.0918 | 0.4949 | 0.2504 | 0.3759 | 0.208 | 0.3954 | 0.3405 | 0.5324 |
| 117.1144 | 8.0 | 856 | 12.5330 | 0.2909 | 0.5328 | 0.2687 | 0.1568 | 0.2262 | 0.4868 | 0.2831 | 0.4625 | 0.5035 | 0.3209 | 0.4428 | 0.686 | 0.5059 | 0.6838 | 0.1762 | 0.5038 | 0.2528 | 0.3978 | 0.1905 | 0.4062 | 0.3289 | 0.5258 |
| 117.1144 | 9.0 | 963 | 12.2873 | 0.3023 | 0.5355 | 0.2927 | 0.1621 | 0.2502 | 0.494 | 0.2851 | 0.4696 | 0.5064 | 0.3301 | 0.452 | 0.6736 | 0.5276 | 0.6932 | 0.1696 | 0.4899 | 0.2633 | 0.4085 | 0.2249 | 0.4154 | 0.326 | 0.5249 |
| 16.4463 | 10.0 | 1070 | 12.2585 | 0.3095 | 0.5506 | 0.3029 | 0.1738 | 0.2405 | 0.4996 | 0.2901 | 0.4721 | 0.5105 | 0.3271 | 0.4558 | 0.6864 | 0.5196 | 0.6892 | 0.2225 | 0.5241 | 0.264 | 0.4022 | 0.2102 | 0.4077 | 0.3309 | 0.5293 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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