Implementation:Open compass VLMEvalKit InstructBLIP

Field	Value
source	VLMEvalKit
domain	Vision, Model_Architecture

Overview

VLM adapter for the InstructBLIP model enabling benchmark evaluation in VLMEvalKit.

Description

InstructBLIP inherits from BaseModel and wraps the InstructBLIP model for use within the VLMEvalKit evaluation framework. It initializes the model and tokenizer/processor from a HuggingFace model path (default: name (config key)) and provides the generate_inner method for inference. Uses LAVIS library for model loading with YAML configuration files.

Usage

Register in vlmeval/config.py via supported_VLM and invoke through the standard evaluation pipeline.

Code Reference

Source: vlmeval/vlm/instructblip.py, Lines: L1-57
Import: from vlmeval.vlm.instructblip import InstructBLIP

Signature:

class InstructBLIP(BaseModel):
    INSTALL_REQ = True
    INTERLEAVE = False
    def __init__(self, model_path='name (config key)', **kwargs): ...
    def generate_inner(self, message, dataset=None): ...

I/O Contract

Direction	Description
Inputs	message — list of dicts with type (text/image) and value; dataset — optional dataset name for custom prompting
Outputs	generate_inner() returns str (model response text)

Usage Examples

from vlmeval.vlm.instructblip import InstructBLIP
model = InstructBLIP(model_path='path/to/model')
response = model.generate_inner(message)

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Principle

Implementation

Heuristic

Environment