Instructions to use facebook/vjepa2-vitg-fpc64-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/vjepa2-vitg-fpc64-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="facebook/vjepa2-vitg-fpc64-384")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("facebook/vjepa2-vitg-fpc64-384") model = AutoModelForMultimodalLM.from_pretrained("facebook/vjepa2-vitg-fpc64-384") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -11,7 +11,7 @@ library_name: transformers
|
|
| 11 |
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of [VJEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/), resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
|
| 12 |
The code is released [in this repository](https://github.com/facebookresearch/vjepa2).
|
| 13 |
|
| 14 |
-
<img src="https://
|
| 15 |
|
| 16 |
## Installation
|
| 17 |
|
|
|
|
| 11 |
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of [VJEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/), resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
|
| 12 |
The code is released [in this repository](https://github.com/facebookresearch/vjepa2).
|
| 13 |
|
| 14 |
+
<img src="https://github.com/user-attachments/assets/914942d8-6a1e-409d-86ff-ff856b7346ab">
|
| 15 |
|
| 16 |
## Installation
|
| 17 |
|