Fine-tune of Qwen2-VL on RICO dataset

Qwen-2VL was trained to predict bounding boxes for elements in images. We further fine-tune it on the RICO android screenshot dataset to improve its performance.

Model Details

Qwen-2VL can use images of any size. We apply random crops to the RICO dataset to ensure a diverse range of aspect ratios and then fine-tune Qwen-2VL to predict bounding boxes of elements in screenshots.

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Thomas Dhome-Casanova
Model type: VLM
Language(s): English
Finetuned from model: Qwen2-VL-7B

Model Sources [optional]

The base model is Qwen2-VL-7B-Instruct

Repository: https://github.com/QwenLM/Qwen2-VL
Paper: https://arxiv.org/pdf/2409.12191

Uses

This model is intended to be used for fast computer-use with strong visual understanding, but limited reasoning capabilities. It should hence be paired with a strong foundational model for reasoning.

How to Get Started with the Model

model = Qwen2VLForConditionalGeneration.from_pretrained( "ThomasDh-C/RicoQwen2VL", torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained("ThomasDh-C/RicoQwen2VL")

Training Details

Training Data

RICO dataset with random crops

Training Procedure

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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