Image Understanding Functions Explained
Core features
- High Resolution Support: Up to 4K resolution images
- Ability to capture details: recognizes minute details in images
- Comparison of multi-chart analysis: Multiple images can be processed and compared at the same time
procedure
- Prepare image files: place the images to be analyzed in a local directory
- Loading models and tokenizers
- Constructing query statements and image paths
- Reasoning with models
- Getting and parsing the returned results
sample code (computing)::
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('internlm/internlm-xcomposer2d5-7b', trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2d5-7b', trust_remote_code=True)
query = "详细分析这张图片"
image = ['examples/dubai.png']
response, _ = model.chat(tokenizer, query, image, do_sample=False, num_beams=3)
print(response)This feature is suitable for a variety of application scenarios such as image annotation, content auditing, and product analysis.
This answer comes from the articleInternLM-XComposer: a multimodal macromodel for outputting very long text and image-video comprehensionThe
May not be reproduced without permission:AI productivity tools " What are the features of InternLM-XComposer for image understanding? How to operate specifically?
