Instructions to use vbius01/detr-resnet-50-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vbius01/detr-resnet-50-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="vbius01/detr-resnet-50-finetuned")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("vbius01/detr-resnet-50-finetuned") model = AutoModelForObjectDetection.from_pretrained("vbius01/detr-resnet-50-finetuned") - Notebooks
- Google Colab
- Kaggle
detr-resnet-50-finetuned
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: 1.7005
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: 1e-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: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.4977 | 1.0 | 150 | 3.6846 |
| 2.3386 | 2.0 | 300 | 2.1265 |
| 2.0291 | 3.0 | 450 | 1.8693 |
| 1.9699 | 4.0 | 600 | 1.7812 |
| 2.0642 | 5.0 | 750 | 1.7608 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for vbius01/detr-resnet-50-finetuned
Base model
facebook/detr-resnet-50