---
license: apache-2.0
---

# AskVideos-VideoCLIPv0.1
Like it's image-only counterpart, CLIP, VideoCLIP enables you to compute a single embedding for videos that can be used to compute similarity with text.

VideoCLIP uses a Video Q-Former to aggregate frame-level embeddings temporally into a single embedding, maintaining relevance of the underlying content. The resulting embedding is then trained with contrastive loss + captioning loss to match it's corresponding text.

# Usage
Link to github to run the model: [link](https://github.com/AskYoutubeAI/AskVideos-VideoCLIP).
```
# Load model.
import video_clip
eval_config = 'eval_configs/video_clip.yaml'
model, vis_processor = video_clip.load_model(eval_config)

# Compute video embeddings.
# video_embs: float matrix of size [num_videos, clip_dim_size, query_tokens] containing VideoCLIP embeddings.
# In this model, clip_dim_size=1024 and query_tokens=32.
video_embs = video_clip.get_all_video_embeddings(videos, model, vis_processor)

# Compute Video-Text similarity.
# v2t_sim: float matrix of size [num_videos, num_texts] indicating similarity.
v2t_sim = video_clip.compute_sim(model, texts, video_embs)

# Compute Text-Video similarity.
# t2v_sim: float matrix of size [num_texts, num_videos] indicating similarity.
t2v_sim = v2t_sim.T

# Compute Video-Video distance.
# v2v_dists: float vector of size [1, num_videos] indicating distance to query video embedding.
v2v_dists = video_clip.compute_dist_videoq(model, video_embs[0], video_embs)
```

For a more detailed demo of how to use the model, see the [colab](https://colab.research.google.com/drive/1kVzoQUS3phupujY-8Bym0nHezRRyd0YQ).