Instructions to use qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad") model = AutoModelForObjectDetection.from_pretrained("qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad") - Notebooks
- Google Colab
- Kaggle
hustvl-yolos-small-finetuned-10k-cppe5-manual-pad
This model is a fine-tuned version of hustvl/yolos-small on the cppe-5 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: 1
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
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Model tree for qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad
Base model
hustvl/yolos-small