Video Classification
Transformers
Safetensors
timesformer
retnet
action-recognition
ucf101
hmdb51
efficient-models
Instructions to use sumit7488/TimesNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumit7488/TimesNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="sumit7488/TimesNet")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("sumit7488/TimesNet") model = AutoModelForVideoClassification.from_pretrained("sumit7488/TimesNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71c4e21459707f3a6657a05d303b1f65136879653e84d0aea7d568d2679c4e54
- Size of remote file:
- 1.03 GB
- SHA256:
- 553b8b6cd94c2783cdec76613820a73477c545f9c91d94374c0b9ae98eac7d7c
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