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https://github.com/illright/draw-ill-help

A drawing app with shape correction powered by ML
https://github.com/illright/draw-ill-help

feature-sliced svelte sveltekit tensorflowjs typescript yolo

Last synced: 3 months ago
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A drawing app with shape correction powered by ML

Host: GitHub
URL: https://github.com/illright/draw-ill-help
Owner: illright
License: mit
Created: 2021-12-10T13:32:24.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2023-10-08T19:35:09.000Z (about 1 year ago)
Last Synced: 2023-10-08T20:29:26.762Z (about 1 year ago)
Topics: feature-sliced, svelte, sveltekit, tensorflowjs, typescript, yolo
Language: TypeScript
Homepage: https://illright.github.io/draw-ill-help/
Size: 7.24 MB
Stars: 15
Watchers: 3
Forks: 0
Open Issues: 13
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Draw, I'll Help

A progressive web application for drawing that automatically corrects hand-drawn circles and rectangles with the power of YOLOv5.

Check it out at .

## Highlights

- 🏎️ Completely serverless
- 🌐 Works offline
- 🧠 Runs YOLOv5 Nano in the background
- 🗃️ Built-in dataset generator
- 😎 Automatic dark mode
- 🍰 Follows the principles of [Feature-Sliced Design](https://feature-sliced.design/)

## Screenshots

Home page
Drawing canvas
Dataset generator

![Home page](./.github/readme/home.jpg)

![Drawing page](./.github/readme/draw.jpg)

![Dataset page](./.github/readme/dataset.jpg)

## Model performance

Since the selected model is the smallest in the family of YOLOv5, it has very small inference times and file size. However, that also has an impact on its predictive capabilities.

See the charts below for the main performance characteristics of the model during training. The model had trained for around 350 epochs, early stopping with a patience value of 100 epochs due to no improvement at the target metric (0.9 of **mAP (0.5:0.95)** and 0.1 of **mAP (0.5)**). The horizontal axis represents the epochs (aka steps).

mAP (0.5)
mAP (0.5:0.95)
Precision
Recall

Mean average precision graph, IoU threshold of 0.5. Starting low at 0.3, rising rapidly to 0.9 after 100 steps, staying around 0.93 until the end with a few sharp drops to almost 0.3 at 160, 195, 260 and 300 steps.

Mean average precision graph, averaged across IoU thresholds from 0.5 to 0.95. Starting low at 0.1, rising almost linearly to 0.7 after 100 steps, peaking in 0.89 at step 250 with a few sharp drops to almost 0.1 at 160, 195, 260 and 300 steps.

Precision graph. Increasing sharply from 0.2 to 0.8 in 20 steps, peaking in 0.92 at step 250 with a few sharp drops at the same steps where mAP had dropped.

Recall graph. Starting low at 0.2, rising almost rapidly to 0.9 after 50 steps, frequently approaching 1.0, first at step 105, with a few sharp drops at the same steps where mAP had dropped.

## Running locally

Grab the `model.zip` file from the latest release and unpack its contents into `static/`. Then run the usual commands:

```bash
pnpm i
pnpm dev
```

## Training YOLOv5 yourself

The Jupyter notebook with explanations for the training process is available in the [`yolov5/`](./yolov5) directory.

The recommended way to run the notebook is with Google Colaboratory. If you're using the GPU session and the free Colab account, the dataset of around 1.5k 416x416 images should take around two hours to train.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/illright/draw-ill-help/blob/main/yolov5/training.ipynb)

## License

The source code of this project is distributed under the terms of the MIT license. [Click here](https://choosealicense.com/licenses/mit/) to learn what that means.