Instructions to use akar49/deform_detr-crack-I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akar49/deform_detr-crack-I with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="akar49/deform_detr-crack-I")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("akar49/deform_detr-crack-I") model = AutoModelForObjectDetection.from_pretrained("akar49/deform_detr-crack-I") - Notebooks
- Google Colab
- Kaggle
deform_detr-crack-I
This model is a fine-tuned version of facebook/deformable-detr-box-supervised on the crack_detection-merged 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
- 10
Model tree for akar49/deform_detr-crack-I
Base model
facebook/deformable-detr-box-supervised