Instructions to use Nick1EST/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nick1EST/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Nick1EST/detr_finetuned_cppe5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Nick1EST/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("Nick1EST/detr_finetuned_cppe5") - Notebooks
- Google Colab
- Kaggle
detr_finetuned_cppe5
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4837
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: 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 250 | 2.1250 |
| 2.4894 | 2.0 | 500 | 1.8966 |
| 2.4894 | 3.0 | 750 | 1.7684 |
| 2.0061 | 4.0 | 1000 | 1.6080 |
| 2.0061 | 5.0 | 1250 | 1.5551 |
| 1.7506 | 6.0 | 1500 | 1.5337 |
Framework versions
- Transformers 5.6.1
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for Nick1EST/detr_finetuned_cppe5
Base model
facebook/detr-resnet-50