Instructions to use grantpitt/autotagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grantpitt/autotagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="grantpitt/autotagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("grantpitt/autotagger") model = AutoModel.from_pretrained("grantpitt/autotagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from grantpitt/autotagger: direct link, hf CLI and curl.
- Browser
- Download file 397 Bytes
-
https://huggingface.co/grantpitt/autotagger/resolve/7e560f0a65b5542cfa70c044a20955134cbac441/preprocessor_config.json
- Command line
-
hf download hf://grantpitt/autotagger@7e560f0a65b5542cfa70c044a20955134cbac441/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/grantpitt/autotagger/resolve/7e560f0a65b5542cfa70c044a20955134cbac441/preprocessor_config.json
397 Bytes
| { | |
| "crop_size": 224, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "VisionTextDualEncoderProcessor", | |
| "resample": 3, | |
| "size": 224 | |
| } | |