Instructions to use Yethi/UIED_DETR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yethi/UIED_DETR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Yethi/UIED_DETR")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Yethi/UIED_DETR") model = AutoModelForObjectDetection.from_pretrained("Yethi/UIED_DETR") - Notebooks
- Google Colab
- Kaggle
UIED_DETR
This model is a fine-tuned version of facebook/detr-resnet-50 on the None dataset.
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.0
- Tokenizers 0.13.3
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