| --- |
| license: mit |
| pipeline_tag: video-classification |
| --- |
| |
| ## Introduction |
|
|
| This repository contains the 6B model of the paper [InternVideo2](https://arxiv.org/pdf/2403.15377) in stage 2. |
|
|
| Code: https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/multi_modality |
| |
| ## 🚀 Installation |
| |
| Please refer to https://github.com/OpenGVLab/InternVideo/blob/main/InternVideo2/multi_modality/INSTALL.md |
|
|
| ## Usage |
|
|
| ```python |
| import cv2 |
| from transformers import AutoModel |
| from modeling_internvideo2 import (retrieve_text, vid2tensor, _frame_from_video,) |
| |
| |
| model = AutoModel.from_pretrained("OpenGVLab/InternVideo2-Stage2_6B", trust_remote_code=True).eval() |
| |
| video = cv2.VideoCapture('example1.mp4') |
| frames = [x for x in _frame_from_video(video)] |
| text_candidates = ["A playful dog and its owner wrestle in the snowy yard, chasing each other with joyous abandon.", |
| "A man in a gray coat walks through the snowy landscape, pulling a sleigh loaded with toys.", |
| "A person dressed in a blue jacket shovels the snow-covered pavement outside their house.", |
| "A cat excitedly runs through the yard, chasing a rabbit.", |
| "A person bundled up in a blanket walks through the snowy landscape, enjoying the serene winter scenery."] |
| |
| texts, probs = retrieve_text(frames, text_candidates, model=model, topk=5) |
| for t, p in zip(texts, probs): |
| print(f'text: {t} ~ prob: {p:.4f}') |
| |
| vidtensor = vid2tensor('example1.mp4', fnum=4) |
| feat = model.get_vid_feat(vidtensor) |
| ``` |
