If you’re looking to optimize your video search capabilities using text queries, the CLIP4Clip model is your go-to solution. It’s trained on a plethora of video-text pairs, making it an excellent tool for large-scale applications. Let’s delve into how you can harness this powerful model for your projects.
Getting Started
The first step is to set up your environment and get access to the correct libraries. Ensure you have the Hugging Face Transformers library installed, as we will be using it to interact with the model.
Extracting Text Embeddings
To begin, you’ll need to extract text embeddings from the model. Consider embedding text like placing an order at a restaurant—you specify your request, and the chef prepares a dish based on your description. Similarly, in this case, the CLIP4Clip model processes your text input to generate embeddings that can be used for video retrieval. Here’s the code you need:
import numpy as np
import torch
from transformers import CLIPTokenizer, CLIPTextModelWithProjection
search_sentence = "a basketball player performing a slam dunk"
model = CLIPTextModelWithProjection.from_pretrained("Searchium-ai/clip4clip-webvid150k")
tokenizer = CLIPTokenizer.from_pretrained("Searchium-ai/clip4clip-webvid150k")
inputs = tokenizer(text=search_sentence, return_tensors='pt')
outputs = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
# Normalize embeddings for retrieval
final_output = outputs[0]
final_output = final_output.norm(dim=-1, keepdim=True)
final_output = final_output.cpu().detach().numpy()
print("Final output:", final_output)
Extracting Video Embeddings
To extract video embeddings, you can refer to the additional notebook available at GSI_VideoRetrieval_VideoEmbedding.ipynb. This notebook provides detailed instructions on preprocessing videos and extracting embeddings. Think of this as setting the cooking time for different dishes—each video might need unique treatment for optimal results.
Understanding Model’s Intended Use
This model excels in large-scale video-text retrieval applications. For practical demonstrations, you can explore the Video Search Space, showcasing around 1.5 million videos. The model’s efficiency shines in retrieving videos based on text queries, making it a valuable asset for businesses handling video datasets.
Evaluation Metrics
The CLIP4Clip model has undergone various evaluations to gauge its capabilities among different video datasets. Here’s a comparison:
| Model | R1 | R5 | R10 | MedianR | MeanR |
|---|---|---|---|---|---|
| Zero-shot clip weights | 37.16 | 62.10 | 71.16 | 3.0 | 42.21 |
| CLIP4Clip weights trained on MSR-VTT | 38.38 | 62.89 | 72.01 | 3.0 | 39.30 |
| CLIP4Clip trained on 150k WebVid | 50.74 | 77.30 | 85.05 | 1.0 | 14.95 |
| Binarized CLIP4Clip trained on 150k WebVid | 50.56 | 76.39 | 83.51 | 1.0 | 43.29 |
Troubleshooting
While working with CLIP4Clip, you might encounter some challenges. Here are a few troubleshooting tips that can help:
- Ensure all required libraries are installed and updated.
- Check the spelling and syntax of your input strings; even a small mistake can lead to errors.
- If your model is not loading, double-check your model path to ensure it is correct.
- If you’ve installed additional dependencies, make sure they don’t conflict with existing libraries.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.