Instructions to use Thastp/efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b0", 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_b0", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "EfficientNetModelForImageClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_efficientnet.EfficientNetConfig", | |
| "AutoModel": "modeling_efficientnet.EfficientNetModel", | |
| "AutoModelForImageClassification": "modeling_efficientnet.EfficientNetModelForImageClassification" | |
| }, | |
| "global_pool": "avg", | |
| "model_name": "efficientnet_b0", | |
| "model_type": "efficientnet", | |
| "num_classes": 1000, | |
| "pretrained": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.50.3" | |
| } | |