Qwen2-VL-7B-Instruct

The Qwen2-VL-7B-Instruct model is a cutting-edge vision-language model from the Qwen family, designed to understand and interact with both visual and textual data. It builds upon the foundation of previous Qwen-VL models and introduces several key enhancements. This model is instruction-tuned and contains 7 billion parameters.

Key Features of Qwen2-VL-7B-Instruct

• Enhanced Visual Understanding: Qwen2-VL is capable of recognizing common objects like plants, animals, and insects, as well as analyzing text, charts, icons, graphics, and layouts within images

• Qwen2-VL can generate structured outputs for data like invoices, forms, and tables, which is useful for applications in finance and commerce

• Object Recognition: The model is proficient in recognizing common objects such as flowers, birds, fish, and insects.

• Image Analysis: Beyond object recognition, Qwen2-VL can analyze texts, charts, icons, graphics, and layouts within images.

• The model can act as a visual agent, reasoning and directing tools for computer and phone use

• The model can accurately locate objects in an image by generating bounding boxes or points and provide stable JSON outputs for coordinates and attributes

• The model supports a wide range of input resolutions. You can adjust the min_pixels and max_pixels to balance performance and computation cost. You can also directly set the resized_height and resized_width

• he model shows strong performance on various image and video benchmarks. For example, it achieves a score of 60 on the MMMUval benchmark, 95.7 on the DocVQAtest benchmark, and 69.6 on the MVBench benchmark.

Limitation of Qwen2-VL-7B-Instruct

The Qwen2-VL-7B-Instruct model, while powerful, does have some limitations:

• Data Timeliness: The image dataset used to train the model is only updated until June 2023. Therefore, information after this date may not be covered by the model.

• Limited Recognition of Individuals and Intellectual Property (IP): The model has a limited capacity to recognize specific individuals or IPs. It may not be able to identify all well-known personalities or brands.

• Limited Capacity for Complex Instructions: The model's understanding and execution capabilities may require improvement when faced with intricate, multi-step instructions.

• Insufficient Counting Accuracy: The model's accuracy in counting objects, especially in complex scenes, is not high.

• Weak Spatial Reasoning Skills: The model's ability to infer positional relationships between objects, particularly in 3D spaces, is inadequate. It may have difficulty judging the relative positions of objects.

• YaRN impact: While the model supports the use of YaRN for processing long texts, it has a significant negative impact on the performance of temporal and spatial localization tasks and is not recommended.