Instructions to use amSOwO/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amSOwO/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="amSOwO/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("amSOwO/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("amSOwO/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from amSOwO/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 12.7 kB
-
https://huggingface.co/amSOwO/detr_finetuned_cppe5/resolve/main/README.md
- Command line
-
hf download hf://amSOwO/detr_finetuned_cppe5/README.md
-
curl -L -o README.md https://huggingface.co/amSOwO/detr_finetuned_cppe5/resolve/main/README.md
12.7 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: microsoft/conditional-detr-resnet-50 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: detr_finetuned_cppe5 | |
| 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_cppe5 | |
| This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.5826 | |
| - Map: 0.0003 | |
| - Map 50: 0.0007 | |
| - Map 75: 0.0 | |
| - Map Small: 0.0014 | |
| - Map Medium: 0.0001 | |
| - Map Large: 0.0 | |
| - Mar 1: 0.0 | |
| - Mar 10: 0.0021 | |
| - Mar 100: 0.0075 | |
| - Mar Small: 0.0118 | |
| - Mar Medium: 0.0062 | |
| - Mar Large: 0.01 | |
| - Map Coverall: 0.0 | |
| - Mar 100 Coverall: 0.0 | |
| - Map Face Shield: 0.0 | |
| - Mar 100 Face Shield: 0.0 | |
| - Map Gloves: 0.0015 | |
| - Mar 100 Gloves: 0.0375 | |
| - Map Goggles: 0.0 | |
| - Mar 100 Goggles: 0.0 | |
| - Map Mask: 0.0 | |
| - Mar 100 Mask: 0.0 | |
| ## 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: 0.001 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 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 | 3.5856 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0061 | 0.0006 | 0.0065 | 0.0157 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0307 | | |
| | No log | 2.0 | 214 | 3.7297 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0078 | 0.0097 | 0.011 | 0.0017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0391 | | |
| | No log | 3.0 | 321 | 3.8304 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0044 | 0.0 | 0.0032 | 0.0149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0222 | | |
| | No log | 4.0 | 428 | 3.6958 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0004 | 0.0071 | 0.0059 | 0.0105 | 0.0047 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0356 | | |
| | 2.8722 | 5.0 | 535 | 4.0271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0017 | 0.0034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0071 | | |
| | 2.8722 | 6.0 | 642 | 3.6288 | 0.0 | 0.0001 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.001 | 0.0125 | 0.01 | 0.0159 | 0.014 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0627 | | |
| | 2.8722 | 7.0 | 749 | 3.6254 | 0.0 | 0.0001 | 0.0 | 0.0001 | 0.0 | 0.0001 | 0.0 | 0.0005 | 0.0063 | 0.0105 | 0.0035 | 0.0132 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0312 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.8722 | 8.0 | 856 | 3.8407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0016 | 0.0 | 0.0017 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.008 | | |
| | 2.8722 | 9.0 | 963 | 3.6509 | 0.0 | 0.0002 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0066 | 0.0072 | 0.007 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.033 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5968 | 10.0 | 1070 | 3.6992 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0003 | 0.0033 | 0.0033 | 0.0041 | 0.0014 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0165 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5968 | 11.0 | 1177 | 3.6423 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.006 | 0.0006 | 0.0097 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0298 | | |
| | 2.5968 | 12.0 | 1284 | 3.5987 | 0.0005 | 0.0023 | 0.0 | 0.0019 | 0.0001 | 0.0 | 0.0002 | 0.0009 | 0.0077 | 0.0092 | 0.0066 | 0.0114 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0024 | 0.0384 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5968 | 13.0 | 1391 | 3.6200 | 0.0 | 0.0001 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0028 | 0.0076 | 0.0099 | 0.0077 | 0.0068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0379 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5968 | 14.0 | 1498 | 3.7723 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0003 | 0.0006 | 0.0044 | 0.0007 | 0.0044 | 0.0077 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0219 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5557 | 15.0 | 1605 | 3.6236 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0011 | 0.0081 | 0.0021 | 0.0099 | 0.0162 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0404 | | |
| | 2.5557 | 16.0 | 1712 | 3.6496 | 0.0001 | 0.0002 | 0.0 | 0.0008 | 0.0003 | 0.0 | 0.0004 | 0.0017 | 0.0065 | 0.0059 | 0.0066 | 0.0077 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0326 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5557 | 17.0 | 1819 | 3.6098 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0009 | 0.0046 | 0.0007 | 0.0039 | 0.0105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0232 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5557 | 18.0 | 1926 | 3.6017 | 0.0001 | 0.0003 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0004 | 0.0015 | 0.0061 | 0.0026 | 0.0069 | 0.0068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0304 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5084 | 19.0 | 2033 | 3.5866 | 0.0003 | 0.0006 | 0.0004 | 0.0045 | 0.0002 | 0.0 | 0.0013 | 0.0029 | 0.0078 | 0.0066 | 0.0079 | 0.0095 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0388 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5084 | 20.0 | 2140 | 3.6129 | 0.0002 | 0.0022 | 0.0 | 0.0008 | 0.0001 | 0.0 | 0.0001 | 0.0015 | 0.0073 | 0.0066 | 0.0069 | 0.0105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.0366 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5084 | 21.0 | 2247 | 3.5974 | 0.0004 | 0.001 | 0.0 | 0.002 | 0.0002 | 0.0 | 0.0004 | 0.0033 | 0.0083 | 0.0099 | 0.0086 | 0.0077 | 0.0 | 0.0 | 0.0 | 0.0 | 0.002 | 0.0415 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5084 | 22.0 | 2354 | 3.5810 | 0.0 | 0.0002 | 0.0 | 0.0001 | 0.0001 | 0.0 | 0.0 | 0.0012 | 0.0075 | 0.0072 | 0.0069 | 0.0109 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0375 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.5084 | 23.0 | 2461 | 3.6132 | 0.0005 | 0.0013 | 0.0 | 0.0016 | 0.0002 | 0.0 | 0.0 | 0.0028 | 0.0077 | 0.0079 | 0.0079 | 0.0082 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0384 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4812 | 24.0 | 2568 | 3.5863 | 0.0002 | 0.0022 | 0.0 | 0.0009 | 0.0001 | 0.0 | 0.0001 | 0.0018 | 0.0069 | 0.0079 | 0.007 | 0.0068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.0344 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4812 | 25.0 | 2675 | 3.6469 | 0.0002 | 0.0022 | 0.0 | 0.0008 | 0.0001 | 0.0 | 0.0001 | 0.0018 | 0.007 | 0.0059 | 0.0076 | 0.0068 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.0348 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4812 | 26.0 | 2782 | 3.5896 | 0.0001 | 0.0006 | 0.0 | 0.0004 | 0.0001 | 0.0 | 0.0 | 0.0017 | 0.007 | 0.0099 | 0.0063 | 0.0082 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0348 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4812 | 27.0 | 2889 | 3.5994 | 0.0001 | 0.0005 | 0.0 | 0.0007 | 0.0001 | 0.0 | 0.0004 | 0.0024 | 0.0074 | 0.0112 | 0.0066 | 0.0086 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0006 | 0.0371 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4812 | 28.0 | 2996 | 3.5806 | 0.0002 | 0.0006 | 0.0 | 0.0014 | 0.0001 | 0.0 | 0.0 | 0.0019 | 0.0072 | 0.0112 | 0.0058 | 0.0105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.0362 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4512 | 29.0 | 3103 | 3.5821 | 0.0002 | 0.0006 | 0.0 | 0.0014 | 0.0001 | 0.0 | 0.0004 | 0.0023 | 0.0075 | 0.0118 | 0.0062 | 0.01 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.0375 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | 2.4512 | 30.0 | 3210 | 3.5826 | 0.0003 | 0.0007 | 0.0 | 0.0014 | 0.0001 | 0.0 | 0.0 | 0.0021 | 0.0075 | 0.0118 | 0.0062 | 0.01 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0015 | 0.0375 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.55.4 | |
| - Pytorch 2.8.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.4 | |