Instructions to use kd13/Modern-MobileNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-MobileNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-MobileNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-MobileNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class MobileNetV1Config(PretrainedConfig): | |
| model_type = "mobilenet_custom" | |
| def __init__( | |
| self, | |
| num_classes: int = 200, | |
| block_dropout: float = 0.0, | |
| final_dropout: float = 0.50, | |
| **kwargs | |
| ): | |
| self.num_classes = num_classes | |
| self.block_dropout = block_dropout | |
| self.final_dropout = final_dropout | |
| super().__init__(**kwargs) |