Instructions to use deschamps-g/vivit-surf-analytics-runpod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deschamps-g/vivit-surf-analytics-runpod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="deschamps-g/vivit-surf-analytics-runpod")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("deschamps-g/vivit-surf-analytics-runpod") model = AutoModelForVideoClassification.from_pretrained("deschamps-g/vivit-surf-analytics-runpod") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_valid_processor_keys": [ | |
| "videos", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_center_crop", | |
| "crop_size", | |
| "do_rescale", | |
| "rescale_factor", | |
| "offset", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "VivitImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "offset": true, | |
| "resample": 2, | |
| "rescale_factor": 0.00784313725490196, | |
| "size": { | |
| "shortest_edge": 224 | |
| } | |
| } | |