Instructions to use kanlo/videomae-base-ASD_Behavour_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanlo/videomae-base-ASD_Behavour_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="kanlo/videomae-base-ASD_Behavour_v4")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("kanlo/videomae-base-ASD_Behavour_v4") model = AutoModelForVideoClassification.from_pretrained("kanlo/videomae-base-ASD_Behavour_v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kanlo/videomae-base-ASD_Behavour_v4: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/kanlo/videomae-base-ASD_Behavour_v4/resolve/main/training_args.bin
- Command line
-
hf download hf://kanlo/videomae-base-ASD_Behavour_v4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kanlo/videomae-base-ASD_Behavour_v4/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 9d541d4db2c94c9400dddc7abd4d42c219f5b79d8c0a60691bb0ed7182096493
- Size of remote file:
- 5.18 kB
- SHA256:
- 34160e405551a9f0290cfc2b7e6b6c598c3ceb7f5a832ee77a586299d475ddf0
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