Instructions to use p1atdev/wd-swinv2-tagger-v3-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/wd-swinv2-tagger-v3-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="p1atdev/wd-swinv2-tagger-v3-hf", 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("p1atdev/wd-swinv2-tagger-v3-hf", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("p1atdev/wd-swinv2-tagger-v3-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor_type": "WDTaggerImageProcessor", | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_tagger.WDTaggerImageProcessor" | |
| }, | |
| "color": [ | |
| 255, | |
| 255, | |
| 255 | |
| ], | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |