Code for object recognition.

[kai-wen-yang]

Hi, thanks for the awesome work. When will you release the code for object recognition?

[williamberrios]

Hi @kai-wen-yang, thanks for your interest! We'll release code for object recognition and vision-language tasks this week.

[kai-wen-yang]

Hi, sorry to bother you, this week is almost over.

[kai-wen-yang]

@williamberrios

[williamberrios]

Hi, sorry for the delay. We are working on bringing a flexible version for evaluating Lens.

[kai-wen-yang]

Would you please tell me the way you perform object recognition so that I can try to implement it by myself?
Did you calculate the negative log likelihood of llm for each class, and then select the one with the smallest negative log likelihood as your prediction?
Or did you directly use llm to generate the class according to tags and attributes?

[williamberrios]

In object recognition, we compute the negative log-likelihood (NLL) for each class and choose the smallest value. Refer to Figure LENS for image classification in the readme for better understanding.

[shuhao02]

Hi, thanks for your fantastic work!
I'm curious about the "negative log-likelihood (NLL) for each class" when the LLM is based on the autoregressive model that generates tokens one by one. Did you calculate the NLL of one class by averaging the tokens' NLL?
Would you please give more details about the calculation?