PyTorch
image-to-svg
logo
vectorization

Im2Vec β€” Logo Raster-to-SVG

A PyTorch model that converts a raster logo (PNG/JPEG) into an editable SVG vector file.

Model

  • Architecture: ResNet-18 encoder + autoregressive Transformer decoder (6 layers, 8 heads, d_model=512) that emits SVG drawing tokens.
  • Checkpoint: model_epoch100.pt β€” trained 100 epochs on ~3,500 FIGR-8 logos.

Usage

See the logo-to-svg Space for a ready-to-use drag-and-drop demo, or the source repo at github.com/R1ley-w/JPG-to-SVG-web-tool.

Known limitation (current checkpoint): monochrome-only output

model_epoch100.pt was trained only on FIGR-8, which turns out to contain zero color information: every sampled training SVG relies on the SVG default fill (black), with no fill/style attribute at all. The model therefore always predicts black fills, regardless of the input logo's actual colors β€” this is a property of the training data, not a bug in the model code.

Two related bugs in the surrounding code (fixed in the source repo, and in the live Space) made this worse: Image.convert("RGB") doesn't alpha-composite, so transparent input backgrounds were rendered solid black instead of left transparent/white; and the SVG tokenizer ignored CSS style="fill:...;" paint declarations.

A colored training set, starvector/svg-emoji (10k rows, full color, mixed permissive licenses), and the pipeline fixes are available in the source repo now. A retrained, color-capable checkpoint has not been produced yet β€” training needs a GPU, which wasn't available when these fixes were made.

Dataset

Trained on FIGR-8, a logo/pictogram SVG dataset licensed for non-commercial use only. This model is therefore distributed under CC BY-NC 4.0.

A future checkpoint trained on starvector/svg-emoji would additionally be bound by that dataset's mixed licenses (Twemoji CC-BY 4.0, Noto Emoji Apache-2.0/OFL, OpenMoji CC BY-SA 4.0 share-alike).

Limitations

  • Output is normalized to a 256Γ—256 canvas; non-square inputs are squashed.
  • Supports flat fills and simple strokes; no gradients, patterns, or text.
  • Current checkpoint outputs black/monochrome shapes only β€” see above.
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Datasets used to train R1l3y-w/im2vec-logo