InternVideo2 Stage2 6B

Usage

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)

model-00003-of-00013.safetensors

safetensors · 1,83 GB · SHA-256 bc1ad5944716…5639 · Hugging Face

--- 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) ```