| --- |
| tags: |
| - image-classification |
| - timm |
| - chart |
| - charts |
| - fintwit |
| - stocks |
| - crypto |
| - finance |
| - financial |
| - financial charts |
| - graphs |
| - financial graphs |
| - plot |
| - plots |
| - financial plots |
| - cryptocurrency |
| - image-recognition |
| - recognition |
| library_name: timm |
| license: mit |
| datasets: |
| - StephanAkkerman/crypto-charts |
| - StephanAkkerman/stock-charts |
| - StephanAkkerman/fintwit-images |
| language: |
| - en |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: chart-recognizer |
| results: |
| - task: |
| type: image-classification |
| dataset: |
| name: Test Set |
| type: images |
| metrics: |
| - type: accuracy |
| value: 0.9782 |
| - type: f1 |
| value: 0.9685 |
| pipeline_tag: image-classification |
| base_model: timm/efficientnet_b0.ra_in1k |
| --- |
| # Chart Recognizer |
|
|
| chart-recognizer is a finetuned model for classifying images. It uses efficientnet as its base model, making it a fast and small model. |
| This model is trained on my own dataset of financial charts posted on Twitter, which can be found here [StephanAkkerman/fintwit-charts](https://huggingface.co/datasets/StephanAkkerman/fintwit-charts). |
|
|
| ## Intended Uses |
|
|
| chart-recognizer is intended for classifying images, mainly images posted on social media. |
|
|
| ## Dataset |
|
|
| chart-recognizer has been trained on my own dataset. So far I have not been able to find another image dataset about financial charts. |
| - [StephanAkkerman/crypto-charts](https://huggingface.co/datasets/StephanAkkerman/crypto-charts): 4,880 images. |
| - [StephanAkkerman/stock-charts](https://huggingface.co/datasets/StephanAkkerman/stock-charts): 5,203 images. |
| - [StephanAkkerman/fintwit-images](https://huggingface.co/datasets/StephanAkkerman/fintwit-images): 4,579 images. |
|
|
| ### Example Images |
| The following images are not part of the training set and can be used for testing purposes. |
|
|
| #### Chart |
|  |
|
|
| #### Non-Chart |
| This can be any image that does not represent a (financial) chart. |
|  |
|
|
| ## More Information |
|
|
| For a comprehensive overview, including the training setup and analysis of the model, visit the [chart-recognizer GitHub repository](https://github.com/StephanAkkerman/chart-recognizer). |
|
|
| ## Usage |
|
|
| Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model can be converted into a pipeline for image classification. |
|
|
| ```python |
| import timm |
| import torch |
| from PIL import Image |
| from timm.data import resolve_data_config, create_transform |
| |
| # Load and set model to eval mode |
| model = timm.create_model("hf_hub:StephanAkkerman/chart-recognizer", pretrained=True) |
| model.eval() |
| |
| # Create transform and get labels |
| transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model)) |
| labels = model.pretrained_cfg["label_names"] |
| |
| # Load and preprocess image |
| image = Image.open("img/examples/tweet_example.png").convert("RGB") |
| x = transform(image).unsqueeze(0) |
| |
| # Get model output and apply softmax |
| probabilities = torch.nn.functional.softmax(model(x)[0], dim=0) |
| |
| # Map probabilities to labels |
| output = {label: prob.item() for label, prob in zip(labels, probabilities)} |
| |
| # Print the predicted probabilities |
| print(output) |
| ``` |
|
|
| ## Citing & Authors |
|
|
| If you use chart-recognizer in your research, please cite me as follows: |
|
|
| ``` |
| @misc{chart-recognizer, |
| author = {Stephan Akkerman}, |
| title = {chart-recognizer: A Specialized Image Model for Financial Charts}, |
| year = {2024}, |
| publisher = {GitHub}, |
| journal = {GitHub repository}, |
| howpublished = {\url{https://github.com/StephanAkkerman/chart-recognizer}} |
| } |
| ``` |
|
|
| ## License |
|
|
| This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details. |
