Instructions to use Rookieez/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rookieez/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Rookieez/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Rookieez/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("Rookieez/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Rookieez/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 11.7 kB
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https://huggingface.co/Rookieez/detr_finetuned_cppe5/resolve/main/README.md
- Command line
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hf download hf://Rookieez/detr_finetuned_cppe5/README.md
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curl -L -o README.md https://huggingface.co/Rookieez/detr_finetuned_cppe5/resolve/main/README.md
11.7 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: facebook/detr-resnet-50 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| 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 [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.1426 | |
| - Map: 0.0 | |
| - Map 50: 0.0 | |
| - Map 75: 0.0 | |
| - Map Small: -1.0 | |
| - Map Medium: 0.0 | |
| - Map Large: -1.0 | |
| - Mar 1: 0.0 | |
| - Mar 10: 0.0 | |
| - Mar 100: 0.0 | |
| - Mar Small: -1.0 | |
| - Mar Medium: 0.0 | |
| - Mar Large: -1.0 | |
| - Map Grey Star: -1.0 | |
| - Mar 100 Grey Star: -1.0 | |
| - Map Insect: 0.0 | |
| - Mar 100 Insect: 0.0 | |
| - Map Moon: 0.0 | |
| - Mar 100 Moon: 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: 5e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch 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 Black Star | Mar 100 Black Star | Map Cat | Mar 100 Cat | Map Grey Star | Mar 100 Grey Star | Map Insect | Mar 100 Insect | Map Moon | Mar 100 Moon | Map Unicorn Head | Mar 100 Unicorn Head | Map Unicorn Whole | Mar 100 Unicorn Whole | | |
| |:-------------:|:-----:|:----:|:---------------:|:---:|:------:|:------:|:---------:|:----------:|:---------:|:-----:|:------:|:-------:|:---------:|:----------:|:---------:|:--------------:|:------------------:|:-------:|:-----------:|:-------------:|:-----------------:|:----------:|:--------------:|:--------:|:------------:|:----------------:|:--------------------:|:-----------------:|:---------------------:| | |
| | No log | 1.0 | 9 | 3.0843 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 2.0 | 18 | 2.8919 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 3.0 | 27 | 2.7564 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 4.0 | 36 | 2.7363 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 5.0 | 45 | 2.6145 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 6.0 | 54 | 2.5328 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 7.0 | 63 | 2.5044 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | | |
| | No log | 8.0 | 72 | 2.4637 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 9.0 | 81 | 2.5407 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 10.0 | 90 | 2.4309 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 11.0 | 99 | 2.4180 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 12.0 | 108 | 2.5998 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 13.0 | 117 | 2.4657 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | -1.0 | -1.0 | | |
| | No log | 14.0 | 126 | 2.3398 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 15.0 | 135 | 2.3136 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 16.0 | 144 | 2.2952 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 17.0 | 153 | 2.3616 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 18.0 | 162 | 2.4010 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 19.0 | 171 | 2.3679 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 20.0 | 180 | 2.3450 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 21.0 | 189 | 2.3824 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 22.0 | 198 | 2.2668 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 23.0 | 207 | 2.1832 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 24.0 | 216 | 2.1715 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 25.0 | 225 | 2.1695 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 26.0 | 234 | 2.1456 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 27.0 | 243 | 2.1490 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 28.0 | 252 | 2.1432 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 29.0 | 261 | 2.1424 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| | No log | 30.0 | 270 | 2.1426 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | -1.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.46.1 | |
| - Pytorch 2.5.1 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.2 | |