Video Classification
Transformers
Safetensors
ttvidt
feature-extraction
video
video-representation-learning
self-supervised-learning
motion
temporal-modeling
dinov3
vision-transformer
custom_code
Eval Results (legacy)
Instructions to use KBlueLeaf/TTVidT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBlueLeaf/TTVidT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="KBlueLeaf/TTVidT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KBlueLeaf/TTVidT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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**TT-VidT** is a self-supervised video encoder built for *motion*. A DINOv3 ViT-B/16
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processes every frame independently (the appearance path), while a compact
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**TT-VidT** is a self-supervised video encoder built for *motion*. A DINOv3 ViT-B/16
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processes every frame independently (the appearance path), while a compact
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