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): VideoMAEv2 data 1.35M -> 0.0014B for unit consistency
Browse filesExpress VideoMAEv2 training data in billions (0.0014B) like the other rows.
README.md
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@@ -110,7 +110,7 @@ single-resolution (S) figure is available.
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| SigLIP2 | 10B | 56.1 | 62.3 | 62.9 | <u>89.1</u> | 45.4 | 86.9 |
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| OpenCLIP | 2B | β | β | β | β | β | β |
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| V-JEPA 2.1 | 0.022B | 69.0 | β | β | 85.5 | 47.9 | 87.7 |
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| VideoMAEv2 |
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<sub>β‘ VideoMAEv2 ViT-g: K400 top-1 after **full fine-tuning**, not the frozen attentive-probe protocol used for the other rows.</sub>
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| 110 |
| SigLIP2 | 10B | 56.1 | 62.3 | 62.9 | <u>89.1</u> | 45.4 | 86.9 |
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| 111 |
| OpenCLIP | 2B | β | β | β | β | β | β |
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| V-JEPA 2.1 | 0.022B | 69.0 | β | β | 85.5 | 47.9 | 87.7 |
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| VideoMAEv2 | 0.0014B | β | β | β | β | β | 90.0<sup>β‘</sup> |
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<sub>β‘ VideoMAEv2 ViT-g: K400 top-1 after **full fine-tuning**, not the frozen attentive-probe protocol used for the other rows.</sub>
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