This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the `orgin_detection_val` and `origin_detection` datasets. It is designed for the task of automatically reading electricity meter displays from images in an end-to-end fashion. This project addresses the automatic reading of smart meter images. Traditional methods often rely on a multi-stage pipeline involving object detection (e.g., YOLO) to locate the display area, followed by Optical Character Recognition (OCR) on the cropped region. This approach, while established, suffers from a cumbersome workflow, error propagation from detection to recognition, and poor robustness to image distortions, uneven lighting, and background noise. To overcome these limitations, this model employs an end-to-end **Image-to-Text** paradigm. It treats the meter reading task as a direct generation problem, taking an entire meter image as input and producing the numerical reading (e.g., "8430.6") as a text sequence. This simplifies the pipeline and leverages global image context for higher accuracy.

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