tags:
- vision
- clip
- clip4clip
- video
- retrieval
pipeline_tag: text-to-video
Model Card
Details
This model underwent training using CLIP4Clip, a video retrieval method based on the CLIP framework, as described in the paper here and implemented in the accompanying code.
The training process involved 150,000 videos obtained from the WebVid Dataset, a comprehensive collection of short videos with corresponding textual descriptions sourced from the web.
To adapt the clip model obtained during training, we adjusted the weights and integrated them into the implementation of clip-vit-base-patch32, making certain modifications to the final layers.
Use with Transformers
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextModelWithProjection
search_sentence = "a basketball player performing a slam dunk"
model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
tokenizer = AutoTokenizer.from_pretrained("Diangle/clip4clip-webvid")
inputs = tokenizer(text=search_sentence , return_tensors="pt", padding=True)
outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False)
# Special projection and changing last layers:
text_projection = model.state_dict()['text_projection.weight']
text_embeds = outputs[1] @ text_projection
final_output = text_embeds[torch.arange(text_embeds.shape[0]), inputs["input_ids"].argmax(dim=-1)]
# Normalizing the embeddings:
final_output = final_output / final_output.norm(dim=-1, keepdim=True)
final_output = final_output.cpu().detach().numpy()
sequence_output = final_output / np.sum(final_output**2, axis=1, keepdims=True)
print("sequence_output: ", sequence_output)
Model Use
Intended Use
This model is intended to use for video retrival, look for example this space.
Extra Information
We have For video embedding there is an extra notebook that describes how to embedd videos.
Performance and Limitations
Performance
We have evaluated the performance of differnet models on the last 10k video clips from Webvid database.
| Model | R1 | R5 | R10 | MedianR | MeanR |------------------------|-------|-------|-------|-----|-----|---------| | Zero-shot clip weights | 37.16 | 62.10 | 71.16 | 3.0 | 42.2128 | CLIP4Clip weights trained on msr-vtt | 38.38 | 62.89 | 72.01 | 3.0 |39.3023 | CLIP4Clip trained on 150k Webvid | 50.74 | 77.30 | 85.05 | 1.0 | 14.9535 | Binarized CLIP4Clip trained on 150k Webvid with rerank100 | 50.56 | 76.39 | 83.51 | 1.0 | 43.2964
For more information about the evaluation you can look at this [notebook].