Instructions to use 2nzi/videomae-surf-analytics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 2nzi/videomae-surf-analytics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="2nzi/videomae-surf-analytics")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("2nzi/videomae-surf-analytics") model = AutoModelForVideoClassification.from_pretrained("2nzi/videomae-surf-analytics", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 749 Bytes
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"_valid_processor_keys": [
"videos",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"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": [
0.485,
0.456,
0.406
],
"image_processor_type": "VideoMAEImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
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