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--- |
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tags: |
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- vision |
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- clip |
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- clip4clip |
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- video |
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- retrieval |
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pipeline_tag: text-to-video |
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--- |
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# Model Card |
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## Details |
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This model underwent training using CLIP4Clip, a video retrieval method based on the CLIP framework, as described in the paper [here](https://arxiv.org/pdf/2104.08860.pdf) and implemented in the accompanying [code](https://github.com/ArrowLuo/CLIP4Clip). |
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The training process involved 150,000 videos obtained from the [WebVid Dataset](https://m-bain.github.io/webvid-dataset/), a comprehensive collection of short videos with corresponding textual descriptions sourced from the web. |
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To adapt the clip model obtained during training, we adjusted the weights and integrated them into the implementation of [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32), making certain modifications to the final layers. |
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### Use with Transformers |
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```python |
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import numpy as np |
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import torch |
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from transformers import AutoTokenizer, CLIPTextModelWithProjection |
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search_sentence = "a basketball player performing a slam dunk" |
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model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid") |
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tokenizer = AutoTokenizer.from_pretrained("Diangle/clip4clip-webvid") |
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inputs = tokenizer(text=search_sentence , return_tensors="pt", padding=True) |
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outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False) |
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# Special projection and changing last layers: |
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text_projection = model.state_dict()['text_projection.weight'] |
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text_embeds = outputs[1] @ text_projection |
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final_output = text_embeds[torch.arange(text_embeds.shape[0]), inputs["input_ids"].argmax(dim=-1)] |
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# Normalizing the embeddings: |
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final_output = final_output / final_output.norm(dim=-1, keepdim=True) |
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final_output = final_output.cpu().detach().numpy() |
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sequence_output = final_output / np.sum(final_output**2, axis=1, keepdims=True) |
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print("sequence_output: ", sequence_output) |
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``` |
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## Model Use |
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### Intended Use |
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This model is intended to use for video retrival, look for example this [**space**](https://huggingface.co/spaces/Diangle/Clip4Clip-webvid). |
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### Extra Information |
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For video embedding there is an extra [notebook](https://huggingface.co/Diangle/clip4clip-webvid/blob/main/Notebooks/GSI_VideoRetrieval_EmbedVideos.ipynb) that describes how to embed videos. |
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## Performance and Limitations |
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### Performance |
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We have evaluated the performance of differnet models on the last 10k video clips from Webvid database. |
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| Model | R1 | R5 | R10 | MedianR | MeanR |
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|------------------------|-------|-------|-------|-----|---------| |
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| Zero-shot clip weights | 37.16 | 62.10 | 71.16 | 3.0 | 42.2128 |
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| CLIP4Clip weights trained on msr-vtt | 38.38 | 62.89 | 72.01 | 3.0 |39.3023 |
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| **CLIP4Clip trained on 150k Webvid** | 50.74 | 77.30 | 85.05 | 1.0 | 14.9535 |
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| Binarized CLIP4Clip trained on 150k Webvid with rerank100 | 50.56 | 76.39 | 83.51 | 1.0 | 43.2964 |
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For more information about the evaluation you can look at this [notebook](https://huggingface.co/Diangle/clip4clip-webvid/blob/main/Notebooks/GSI_VideoRetrieval-Evaluation.ipynb). |
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