Instructions to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientNetForImageClassification") 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("hf-internal-testing/tiny-random-EfficientNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": { | |
| "height": 64, | |
| "width": 64 | |
| }, | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "EfficientNetImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "include_top": true, | |
| "resample": 0, | |
| "rescale_factor": 0.00392156862745098, | |
| "rescale_offset": false, | |
| "size": { | |
| "height": 64, | |
| "width": 64 | |
| } | |
| } | |