This is a fintuned model for detecting objects in financial charts. It uses YOLO12n as its base model, making it a fast and small model. This model is trained on my own datasets of financial charts posted on Twitter, which I labeled myself.

Inteded uses

chart-info-detector is inteded for finding relevant information from financial chart images.

An example of a labelled financial chart:

Usage

To use this model you need to install the ultralytics library with Python. You can then download the weights of this repo and load them into the model. The image size during training was set to 1792, so be sure to use this too.

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Load the pre-trained model
model = YOLO(hf_hub_download(
    repo_id="StephanAkkerman/chart-info-detector",
    filename="weights/best.pt",
    repo_type="model",
))

# Perform object detection on an image
results = model.predict(
    source=img_path,
    imgsz=1792,
    conf=0.25,
    iou=0.5,
    verbose=False,
)

r = results[0]

# Show the results 
r.show()