Object Detection
Transformers
TensorBoard
Safetensors
yolos
ppe
hard-hat
safety
construction
Generated from Trainer
Instructions to use ikigaiii/yolos-tiny-ppe-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ikigaiii/yolos-tiny-ppe-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ikigaiii/yolos-tiny-ppe-detection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ikigaiii/yolos-tiny-ppe-detection") model = AutoModelForObjectDetection.from_pretrained("ikigaiii/yolos-tiny-ppe-detection") - Notebooks
- Google Colab
- Kaggle
yolos-tiny-ppe-detection
This model is a fine-tuned version of ikigaiii/yolos-tiny-ppe-detection on the None 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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