Instructions to use popkek00/fall-detection-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use popkek00/fall-detection-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="popkek00/fall-detection-ft") 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("popkek00/fall-detection-ft") model = AutoModelForImageClassification.from_pretrained("popkek00/fall-detection-ft") - Notebooks
- Google Colab
- Kaggle
File size: 633 Bytes
efeb8f6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"crop_pct": 0.875,
"crop_size": null,
"data_format": "channels_first",
"default_to_square": false,
"device": null,
"disable_grouping": null,
"do_center_crop": null,
"do_convert_rgb": null,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "ConvNextImageProcessorFast",
"image_std": [
0.229,
0.224,
0.225
],
"input_data_format": null,
"pad_size": null,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"shortest_edge": 224
}
}
|