Model Overview
Model Features
Model Capabilities
Use Cases
đ detr-resnet-50-finetuned-10k-cppe5
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 is designed for object detection tasks and can achieve relatively good performance in detecting objects in the cppe - 5 dataset.
đ Quick Start
This model is mainly used for object detection. You can load and use it through the Hugging Face Transformers library.
⨠Features
- Fine - tuned: Based on the pre - trained model
facebook/detr - resnet - 50
, it is fine - tuned on the cppe - 5 dataset to better adapt to the object detection tasks of this dataset. - Multiple evaluation metrics: It provides a variety of evaluation metrics, such as Loss, Map, Map 50, etc., to comprehensively evaluate the performance of the model.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
No code examples are provided in the original document.
đ Documentation
Model Performance
It achieves the following results on the evaluation set:
- Loss: 0.9865
- Map: 0.3578
- Map 50: 0.6781
- Map 75: 0.3105
- Map Small: 0.3578
- Map Medium: - 1.0
- Map Large: - 1.0
- Mar 1: 0.365
- Mar 10: 0.535
- Mar 100: 0.5483
- Mar Small: 0.5483
- Mar Medium: - 1.0
- Mar Large: - 1.0
- Map Coverall: 0.6584
- Mar 100 Coverall: 0.7772
- Map Face Shield: 0.3691
- Mar 100 Face Shield: 0.6063
- Map Gloves: 0.2477
- Mar 100 Gloves: 0.4266
- Map Goggles: 0.1766
- Mar 100 Goggles: 0.4655
- Map Mask: 0.3371
- Mar 100 Mask: 0.4661
Training and Evaluation Data
The model is trained on the cppe - 5 dataset. However, more detailed information about the data is not provided in the original document.
Training Procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- num_epochs: 100.0
- 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 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3.7198 | 1.0 | 107 | 3.1869 | 0.0036 | 0.0144 | 0.0006 | 0.0036 | -1.0 | -1.0 | 0.017 | 0.0426 | 0.0647 | 0.0647 | -1.0 | -1.0 | 0.0169 | 0.1994 | 0.0 | 0.0 | 0.0001 | 0.0177 | 0.0 | 0.0 | 0.0008 | 0.1062 |
3.0393 | 2.0 | 214 | 2.8978 | 0.007 | 0.021 | 0.003 | 0.007 | -1.0 | -1.0 | 0.0192 | 0.0796 | 0.1238 | 0.1238 | -1.0 | -1.0 | 0.0333 | 0.5216 | 0.0 | 0.0 | 0.0001 | 0.0292 | 0.0 | 0.0 | 0.0017 | 0.0684 |
2.7812 | 3.0 | 321 | 2.5445 | 0.0138 | 0.0414 | 0.0085 | 0.0138 | -1.0 | -1.0 | 0.0307 | 0.0994 | 0.1258 | 0.1258 | -1.0 | -1.0 | 0.0655 | 0.4938 | 0.0 | 0.0 | 0.0002 | 0.0354 | 0.0 | 0.0 | 0.0033 | 0.1 |
2.5992 | 4.0 | 428 | 2.3828 | 0.0232 | 0.0601 | 0.0155 | 0.0232 | -1.0 | -1.0 | 0.0423 | 0.1202 | 0.1518 | 0.1518 | -1.0 | -1.0 | 0.1021 | 0.5481 | 0.0 | 0.0 | 0.0006 | 0.0495 | 0.0059 | 0.0109 | 0.0072 | 0.1503 |
2.3828 | 5.0 | 535 | 2.2672 | 0.0283 | 0.0703 | 0.0179 | 0.0283 | -1.0 | -1.0 | 0.0521 | 0.1283 | 0.1737 | 0.1737 | -1.0 | -1.0 | 0.1344 | 0.5846 | 0.0 | 0.0 | 0.001 | 0.0833 | 0.0 | 0.0 | 0.0063 | 0.2006 |
2.2633 | 6.0 | 642 | 2.0618 | 0.0479 | 0.0996 | 0.0416 | 0.0479 | -1.0 | -1.0 | 0.0782 | 0.1679 | 0.2035 | 0.2035 | -1.0 | -1.0 | 0.2099 | 0.6333 | 0.003 | 0.0159 | 0.0018 | 0.1187 | 0.0052 | 0.0218 | 0.0195 | 0.2277 |
2.1837 | 7.0 | 749 | 2.1100 | 0.0455 | 0.1159 | 0.0255 | 0.0455 | -1.0 | -1.0 | 0.0747 | 0.1582 | 0.1894 | 0.1894 | -1.0 | -1.0 | 0.2068 | 0.6185 | 0.0085 | 0.0556 | 0.001 | 0.0734 | 0.0002 | 0.0018 | 0.0113 | 0.1977 |
2.0689 | 8.0 | 856 | 2.0000 | 0.054 | 0.1389 | 0.0301 | 0.054 | -1.0 | -1.0 | 0.0954 | 0.1846 | 0.2159 | 0.2159 | -1.0 | -1.0 | 0.2155 | 0.5537 | 0.0314 | 0.1397 | 0.0049 | 0.1406 | 0.0002 | 0.0018 | 0.0181 | 0.2435 |
2.0417 | 9.0 | 963 | 1.8702 | 0.0697 | 0.1631 | 0.0501 | 0.0697 | -1.0 | -1.0 | 0.1074 | 0.2173 | 0.257 | 0.257 | -1.0 | -1.0 | 0.2826 | 0.6086 | 0.0279 | 0.181 | 0.0046 | 0.1734 | 0.0102 | 0.0418 | 0.0234 | 0.2802 |
1.9972 | 10.0 | 1070 | 1.8563 | 0.0742 | 0.1568 | 0.0541 | 0.0742 | -1.0 | -1.0 | 0.1196 | 0.2416 | 0.2786 | 0.2786 | -1.0 | -1.0 | 0.2933 | 0.6086 | 0.0233 | 0.1921 | 0.0053 | 0.1672 | 0.0239 | 0.0891 | 0.025 | 0.3362 |
1.8931 | 11.0 | 1177 | 1.6778 | 0.1054 | 0.2248 | 0.0898 | 0.1054 | -1.0 | -1.0 | 0.1456 | 0.2764 | 0.3033 | 0.3033 | -1.0 | -1.0 | 0.3955 | 0.671 | 0.0498 | 0.2603 | 0.0108 | 0.2188 | 0.0149 | 0.0382 | 0.056 | 0.3282 |
1.8269 | 12.0 | 1284 | 1.6905 | 0.1111 | 0.2399 | 0.0942 | 0.1111 | -1.0 | -1.0 | 0.1543 | 0.2949 | 0.3257 | 0.3257 | -1.0 | -1.0 | 0.4113 | 0.679 | 0.069 | 0.319 | 0.0087 | 0.2021 | 0.015 | 0.0909 | 0.0514 | 0.3373 |
1.8036 | 13.0 | 1391 | 1.6406 | 0.1149 | 0.2407 | 0.097 | 0.1149 | -1.0 | -1.0 | 0.1636 | 0.3108 | 0.3372 | 0.3372 | -1.0 | -1.0 | 0.4255 | 0.6759 | 0.0771 | 0.3381 | 0.0109 | 0.2182 | 0.0137 | 0.1309 | 0.047 | 0.3226 |
1.7463 | 14.0 | 1498 | 1.7169 | 0.1106 | 0.2421 | 0.0875 | 0.1106 | -1.0 | -1.0 | 0.1776 | 0.3205 | 0.3511 | 0.3511 | -1.0 | -1.0 | 0.3996 | 0.7 | 0.0404 | 0.2476 | 0.0117 | 0.2458 | 0.0257 | 0.2036 | 0.0757 | 0.3582 |
1.763 | 15.0 | 1605 | 1.5961 | 0.1245 | 0.2577 | 0.1018 | 0.1245 | -1.0 | -1.0 | 0.1817 | 0.3384 | 0.3677 | 0.3677 | -1.0 | -1.0 | 0.4575 | 0.6679 | 0.0775 | 0.3698 | 0.0107 | 0.2505 | 0.0318 | 0.1964 | 0.0447 | 0.3537 |
1.6467 | 16.0 | 1712 | 1.5365 | 0.1376 | 0.3073 | 0.1062 | 0.1376 | -1.0 | -1.0 | 0.2164 | 0.38 | 0.408 | 0.408 | -1.0 | -1.0 | 0.455 | 0.6852 | 0.0739 | 0.3873 | 0.0215 | 0.2719 | 0.0442 | 0.2891 | 0.0934 | 0.4068 |
1.6222 | 17.0 | 1819 | 1.5990 | 0.1295 | 0.2696 | 0.1026 | 0.1295 | -1.0 | -1.0 | 0.1802 | 0.3409 | 0.3693 | 0.3693 | -1.0 | -1.0 | 0.4577 | 0.6654 | 0.0786 | 0.3619 | 0.0297 | 0.2958 | 0.0211 | 0.2218 | 0.0603 | 0.3017 |
1.6239 | 18.0 | 1926 | 1.4164 | 0.159 | 0.3543 | 0.1262 | 0.159 | -1.0 | -1.0 | 0.235 | 0.3929 | 0.4138 | 0.4138 | -1.0 | -1.0 | 0.4753 | 0.7204 | 0.0921 | 0.3968 | 0.039 | 0.2922 | 0.0323 | 0.2636 | 0.1565 | 0.396 |
1.5448 | 19.0 | 2033 | 1.4689 | 0.1628 | 0.3725 | 0.1314 | 0.1628 | -1.0 | -1.0 | 0.205 | 0.3811 | 0.4064 | 0.4064 | -1.0 | -1.0 | 0.4794 | 0.6895 | 0.1038 | 0.419 | 0.0398 | 0.2828 | 0.0333 | 0.28 | 0.1578 | 0.3605 |
1.5026 | 20.0 | 2140 | 1.4093 | 0.1798 | 0.397 | 0.1369 | 0.1798 | -1.0 | -1.0 | 0.2336 | 0.4125 | 0.4349 | 0.4349 | -1.0 | -1.0 | 0.4851 | 0.6858 | 0.1494 | 0.4508 | 0.0341 | 0.2859 | 0.0434 | 0.3382 | 0.1869 | 0.4136 |
1.4797 | 21.0 | 2247 | 1.4605 | 0.1652 | 0.3605 | 0.1254 | 0.1652 | -1.0 | -1.0 | 0.2295 | 0.3823 | 0.4041 | 0.4041 | -1.0 | -1.0 | 0.4978 | 0.6957 | 0.0968 | 0.3825 | 0.0529 | 0.2797 | 0.0263 | 0.3236 | 0.1522 | 0.339 |
1.4298 | 22.0 | 2354 | 1.4231 | 0.163 | 0.3558 | 0.115 | 0.163 | -1.0 | -1.0 | 0.2256 | 0.3851 | 0.4108 | 0.4108 | -1.0 | -1.0 | 0.4902 | 0.7093 | 0.1033 | 0.4159 | 0.0515 | 0.313 | 0.0261 | 0.3109 | 0.1437 | 0.3051 |
1.4157 | 23.0 | 2461 | 1.3665 | 0.1914 | 0.4048 | 0.1533 | 0.1914 | -1.0 | -1.0 | 0.2478 | 0.4232 | 0.447 | 0.447 | -1.0 | -1.0 | 0.491 | 0.6975 | 0.1599 | 0.4683 | 0.0502 | 0.3021 | 0.0603 | 0.3618 | 0.1956 | 0.4051 |
1.4438 | 24.0 | 2568 | 1.2908 | 0.2103 | 0.433 | 0.168 | 0.2103 | -1.0 | -1.0 | 0.2643 | 0.4512 | 0.4761 | 0.4761 | -1.0 | -1.0 | 0.5368 | 0.7136 | 0.1493 | 0.4873 | 0.0789 | 0.3609 | 0.043 | 0.3891 | 0.2433 | 0.4294 |
1.4044 | 25.0 | 2675 | 1.4752 | 0.1709 | 0.3749 | 0.1388 | 0.1709 | -1.0 | -1.0 | 0.2187 | 0.3926 | 0.4191 | 0.4191 | -1.0 | -1.0 | 0.4862 | 0.7167 | 0.09 | 0.3905 | 0.0762 | 0.299 | 0.0393 | 0.3527 | 0.1627 | 0.3367 |
1.3703 | 26.0 | 2782 | 1.3047 | 0.2162 | 0.4568 | 0.1714 | 0.2162 | -1.0 | -1.0 | 0.2661 | 0.4344 | 0.4548 | 0.4548 | -1.0 | -1.0 | 0.5342 | 0.7272 | 0.166 | 0.4508 | 0.0971 | 0.3281 | 0.0424 | 0.3527 | 0.2414 | 0.4153 |
1.3292 | 27.0 | 2889 | 1.2674 | 0.22 | 0.4681 | 0.1702 | 0.22 | -1.0 | -1.0 | 0.2743 | 0.4286 | 0.4473 | 0.4473 | -1.0 | -1.0 | 0.5438 | 0.7265 | 0.2128 | 0.4429 | 0.1171 | 0.3443 | 0.0387 | 0.3455 | 0.1878 | 0.3774 |
1.359 | 28.0 | 2996 | 1.3156 | 0.2007 | 0.4272 | 0.1536 | 0.2007 | -1.0 | -1.0 | 0.2715 | 0.4384 | 0.4555 | 0.4555 | -1.0 | -1.0 | 0.5306 | 0.7111 | 0.163 | 0.5016 | 0.0896 | 0.3135 | 0.0307 | 0.38 | 0.1898 | 0.3712 |
1.3471 | 29.0 | 3103 | 1.2646 | 0.2161 | 0.455 | 0.172 | 0.2161 | -1.0 | -1.0 | 0.277 | 0.4492 | 0.4728 | 0.4728 | -1.0 | -1.0 | 0.5301 | 0.7216 | 0.1708 | 0.519 | 0.1216 | 0.3271 | 0.0391 | 0.4145 | 0.2189 | 0.3819 |
1.308 | 30.0 | 3210 | 1.3017 | 0.2107 | 0.4465 | 0.1718 | 0.2107 | -1.0 | -1.0 | 0.2556 | 0.4141 | 0.4387 | 0.4387 | -1.0 | -1.0 | 0.5321 | 0.7136 | 0.1531 | 0.454 | 0.1037 | 0.3203 | 0.0334 | 0.3218 | 0.2313 | 0.3836 |
1.3023 | 31.0 | 3317 | 1.2809 | 0.2174 | 0.462 | 0.1714 | 0.2174 | -1.0 | -1.0 | 0.2646 | 0.4242 | 0.4473 | 0.4473 | -1.0 | -1.0 | 0.5484 | 0.7259 | 0.1686 | 0.427 | 0.1163 | 0.3536 | 0.0506 | 0.3564 | 0.2029 | 0.3734 |
1.2561 | 32.0 | 3424 | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
đ§ Technical Details
The model is based on the DETR (Detection Transformer) architecture, which uses a Transformer - based encoder - decoder structure for object detection. During training, the Adam optimizer is used, and a linear learning rate scheduler is adopted. The mixed - precision training technique (Native AMP) is also applied to speed up the training process.
đ License
This model is released under the Apache 2.0 license.









