Instructions to use Thastp/efficientnet_b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b1", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_efficientnet.EfficientNetImageProcessor" | |
| }, | |
| "config": { | |
| "crop_mode": "center", | |
| "crop_pct": 0.9, | |
| "input_size": [ | |
| 3, | |
| 240, | |
| 240 | |
| ], | |
| "interpolation": "bicubic", | |
| "mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ] | |
| }, | |
| "image_processor_type": "EfficientNetImageProcessor", | |
| "model_name": "efficientnet_b1" | |
| } | |