Instructions to use rnud/detr-resnet-50_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rnud/detr-resnet-50_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="rnud/detr-resnet-50_finetuned")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("rnud/detr-resnet-50_finetuned") model = AutoModelForObjectDetection.from_pretrained("rnud/detr-resnet-50_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
detr-resnet-50_finetuned
This model is a fine-tuned version of facebook/detr-resnet-50 on the imagefolder 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: 5e-06
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
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Model tree for rnud/detr-resnet-50_finetuned
Base model
facebook/detr-resnet-50