Instructions to use brainer/detr-resnet-50-dc5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brainer/detr-resnet-50-dc5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="brainer/detr-resnet-50-dc5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("brainer/detr-resnet-50-dc5") model = AutoModelForObjectDetection.from_pretrained("brainer/detr-resnet-50-dc5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from brainer/detr-resnet-50-dc5: direct link, hf CLI and curl.
- Browser
- Download file 986 Bytes
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https://huggingface.co/brainer/detr-resnet-50-dc5/resolve/main/README.md
- Command line
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hf download hf://brainer/detr-resnet-50-dc5/README.md
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curl -L -o README.md https://huggingface.co/brainer/detr-resnet-50-dc5/resolve/main/README.md
986 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50-dc5
results: []
detr-resnet-50-dc5
This model was trained from scratch on an unknown 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0