Image Feature Extraction
Transformers
Safetensors
motif_vision
feature-extraction
motif
vision-transformer
self-supervised
video
custom_code
Instructions to use Motif-Technologies/Motif-Vision-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Motif-Technologies/Motif-Vision-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs(README): fix leftover img alt text to match tracking caption
Browse filesUpdate <img> alt from 'Self-attention visualization' to 'Point tracking: Motif vs V-JEPA 2.1' for consistency with the caption.
README.md
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performance across image and video benchmarks — and leads on DAVIS video tracking.
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<p align="center">
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<img src="assets/haaland_full_attn_blk20.gif" width="480" alt="
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</p>
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<p align="center"><em>Point tracking on a video clip (top: Motif, bottom: V-JEPA 2.1) — a query point propagated across frames by patch-feature cosine similarity. Motif stays locked on the subject noticeably better than V-JEPA 2.1.</em></p>
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performance across image and video benchmarks — and leads on DAVIS video tracking.
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<p align="center">
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<img src="assets/haaland_full_attn_blk20.gif" width="480" alt="Point tracking on a video clip: Motif vs V-JEPA 2.1"/>
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</p>
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<p align="center"><em>Point tracking on a video clip (top: Motif, bottom: V-JEPA 2.1) — a query point propagated across frames by patch-feature cosine similarity. Motif stays locked on the subject noticeably better than V-JEPA 2.1.</em></p>
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