Instructions to use polejowska/yolos-tiny-CD45RB-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use polejowska/yolos-tiny-CD45RB-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="polejowska/yolos-tiny-CD45RB-1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("polejowska/yolos-tiny-CD45RB-1000") model = AutoModelForObjectDetection.from_pretrained("polejowska/yolos-tiny-CD45RB-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from polejowska/yolos-tiny-CD45RB-1000: direct link, hf CLI and curl.
- Browser
- Download file 1.98 kB
-
https://huggingface.co/polejowska/yolos-tiny-CD45RB-1000/resolve/main/README.md
- Command line
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hf download hf://polejowska/yolos-tiny-CD45RB-1000/README.md
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curl -L -o README.md https://huggingface.co/polejowska/yolos-tiny-CD45RB-1000/resolve/main/README.md
1.98 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: yolos-tiny-CD45RB-1000 | |
| 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. --> | |
| # yolos-tiny-CD45RB-1000 | |
| This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.6317 | |
| ## 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: 0.0001 | |
| - 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 | |
| - num_epochs: 15 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 3.4965 | 1.0 | 94 | 2.7799 | | |
| | 3.4526 | 2.0 | 188 | 2.7380 | | |
| | 3.4012 | 3.0 | 282 | 2.6721 | | |
| | 3.2776 | 4.0 | 376 | 2.6651 | | |
| | 3.2164 | 5.0 | 470 | 2.6555 | | |
| | 3.2701 | 6.0 | 564 | 2.6489 | | |
| | 3.1847 | 7.0 | 658 | 2.6993 | | |
| | 3.0959 | 8.0 | 752 | 2.6364 | | |
| | 3.0506 | 9.0 | 846 | 2.6464 | | |
| | 3.0497 | 10.0 | 940 | 2.6304 | | |
| | 3.0767 | 11.0 | 1034 | 2.6344 | | |
| | 3.0397 | 12.0 | 1128 | 2.6142 | | |
| | 2.982 | 13.0 | 1222 | 2.6787 | | |
| | 2.883 | 14.0 | 1316 | 2.6492 | | |
| | 2.8978 | 15.0 | 1410 | 2.6317 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.0 | |
| - Tokenizers 0.13.2 | |