Instructions to use ellabettison/Logo-Detection-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ellabettison/Logo-Detection-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ellabettison/Logo-Detection-finetune")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ellabettison/Logo-Detection-finetune") model = AutoModelForObjectDetection.from_pretrained("ellabettison/Logo-Detection-finetune") - Notebooks
- Google Colab
- Kaggle
Logo-Detection-finetune
This model is a fine-tuned version of hustvl/yolos-small 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch 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: 100
- num_epochs: 20
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Tokenizers 0.21.0
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Model tree for ellabettison/Logo-Detection-finetune
Base model
hustvl/yolos-small