#
DePlot

##
Overview

DePlot was proposed in the paper DePlot: One-shot visual language reasoning by plot-to-table translation from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.

The abstract of the paper states the following:

Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over finetuned SOTA on human-written queries from the task of chart QA.

##
Model description

DePlot is a model that is trained using `Pix2Struct` architecture. You can find more information about `Pix2Struct` in the Pix2Struct documentation. DePlot is a Visual Question Answering subset of `Pix2Struct` architecture. It renders the input question on the image and predicts the answer.

##
Usage

Currently one checkpoint is available for DePlot:

- `google/deplot`: DePlot fine-tuned on ChartQA dataset

    from transformers import AutoProcessor, Pix2StructForConditionalGeneration
    import requests
    from PIL import Image

    model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")
    processor = AutoProcessor.from_pretrained("google/deplot")
    url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png"
    image = Image.open(requests.get(url, stream=True).raw)

    inputs = processor(images=image, text="Generate underlying data table of the figure below:", return_tensors="pt")
    predictions = model.generate(**inputs, max_new_tokens=512)
    print(processor.decode(predictions[0], skip_special_tokens=True))

##
Fine-tuning

To fine-tune DePlot, refer to the pix2struct fine-tuning notebook. For `Pix2Struct` models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faster convergence:

    from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup

    optimizer = Adafactor(self.parameters(), scale_parameter=False, relative_step=False, lr=0.01, weight_decay=1e-05)
    scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=1000, num_training_steps=40000)