> This is the documentation for LibreYOLO 1.5.0. Current version: https://www.libreyolo.com/docs/models/dexined # DexiNed DexiNed (Dense Extreme Inception Network) is a convolutional network that predicts a dense edge-probability map from one RGB image. LibreYOLO wraps its architecture for edge detection only; no checkpoint ships with the library. Tasks: edge. Install: pip install libreyolo. Verified against LibreYOLO v1.5.0. ## Install DexiNed needs no optional extra. Everything it imports is in the base install. ```bash pip install libreyolo ``` ## Predict LibreYOLO ships no DexiNed checkpoint. The officially released weights are trained on BIPED, whose published dataset terms restrict use to non-commercial purposes, so LibreYOLO does not mirror them. Convert a checkpoint you are licensed to use with `weights/convert_dexined_weights.py`, which checks the tensor keys against the runtime architecture before writing a file LibreYOLO can load directly: ```bash python weights/convert_dexined_weights.py upstream.pth weights/LibreDexiNedb-edge.pt --verify ``` **Python** ```python from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("weights/LibreDexiNedb-edge.pt") result = model(SAMPLE_IMAGE, save=True) edges = result.edges print(edges.array.shape) # (H, W) float32 in [0, 1] print(edges.binary(0.5).sum()) # thresholded edge-pixel count ``` **CLI** ```bash libreyolo predict model=weights/LibreDexiNedb-edge.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True ``` `result.edges` holds the result: an `(H, W)` float32 array in `[0, 1]`, with `.binary(threshold)` returning a boolean edge mask. There are no boxes, so `conf`, `iou` and `max_det` have no effect. See [prediction](/docs/v1.5.0/predict) for sources, streaming and result handling. ## Variants DexiNed ships one size in LibreYOLO. LibreYOLO's benchmark harness has not measured this family, so there are no published numbers to compare it against. ## Validate `val()` reports BSDS-style ODS and OIS F-measures against a paired edge dataset: images beside same-stem edge maps, with an optional validity mask so padded pixels never count. `imgsz` must be divisible by the network's downsample stride, and LibreYOLO raises a clear error if it is not. **Python** ```python from libreyolo import LibreYOLO model = LibreYOLO("weights/LibreDexiNedb-edge.pt") metrics = model.val(data="my-dataset.yaml", imgsz=352) print(metrics["metrics/ODS"]) # optimal-dataset-scale F-measure print(metrics["metrics/OIS"]) # optimal-image-scale F-measure ``` **CLI** ```bash libreyolo val model=weights/LibreDexiNedb-edge.pt data=my-dataset.yaml imgsz=352 ``` ## Export | Task | ONNX | TorchScript | ExecuTorch | TensorRT | OpenVINO | Paddle | MNN | RKNN | ncnn | TFLite | CoreML | Core AI | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | edge | yes | yes | yes | yes | yes | | | | | yes | | | A "yes" means the export is supported. An empty cell means the exporter refuses that combination. Edge export uses a fixed-resolution, batch-1 runtime contract: `dynamic` and a `batch` other than 1 are rejected, and the exported graph outputs a single fused probability map. An exported artifact loads back through `LibreYOLO()` on its file suffix, so a `.onnx` file behaves like a checkpoint and returns the same `Results`. **Python** ```python from libreyolo import LibreYOLO model = LibreYOLO("weights/LibreDexiNedb-edge.pt") model.export(format="onnx", imgsz=352) model.export(format="tensorrt", imgsz=352, half=True) ``` **CLI** ```bash libreyolo export model=weights/LibreDexiNedb-edge.pt format=onnx imgsz=352 libreyolo export model=weights/LibreDexiNedb-edge.pt format=tensorrt imgsz=352 half=True ``` **Use the exported file** ```python from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("weights/LibreDexiNedb-edge.onnx") result = model(SAMPLE_IMAGE) print(result.edges.array.shape) ``` ## Licensing Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice. - Original work: DexiNed, Xavier Soria - Upstream license: MIT - Upstream source: https://github.com/xavysp/DexiNed - LibreYOLO code: MIT - Weights: MIT, distributed by their authors. LibreYOLO does not host or mirror them. - Interpretation: MIT is a permissive license, so the DexiNed architecture LibreYOLO ports can be used in commercial and closed-source products, with the license text and copyright notice kept alongside any copy you redistribute. LibreYOLO ships no checkpoint for this family: the officially released weights are trained on BIPED, whose published dataset terms restrict use to non-commercial purposes, and mirroring them would carry that restriction into a nominally MIT-licensed download. Convert a checkpoint you hold a license for with `weights/convert_dexined_weights.py`; the MIT code license does not change the terms attached to whatever checkpoint you convert. LibreYOLO publishes no DexiNed checkpoint. Nothing is mirrored under the LibreYOLO organization; convert a checkpoint you hold a license for with `weights/convert_dexined_weights.py` instead. ## Citation ```bibtex @INPROCEEDINGS {xsoria2020dexined, author = {X. Soria and E. Riba and A. Sappa}, booktitle = {2020 IEEE Winter Conference on Applications of Computer Vision (WACV)}, title = {Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection}, year = {2020}, volume = {}, issn = {}, pages = {1912-1921}, keywords = {image edge detection;convolution;training;feeds;machine learning;task analysis;kernel}, doi = {10.1109/WACV45572.2020.9093290}, url = {https://doi.ieeecomputersociety.org/10.1109/WACV45572.2020.9093290}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = {mar} } @article{soria2023dexined_ext, title = {Dense extreme inception network for edge detection}, journal = {Pattern Recognition}, volume = {139}, pages = {109461}, year = {2023}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2023.109461}, url = {https://www.sciencedirect.com/science/article/pii/S0031320323001619}, author = {Xavier Soria and Angel Sappa and Patricio Humanante and Arash Akbarinia}, keywords = {Edge detection, Deep learning, CNN, Contour detection, Boundary detection, Segmentation} } ``` Copied from https://github.com/xavysp/DexiNed#citation ## DexiNed architecture - Base: [SVG](/diagrams/models/dexined/b-edge.svg), [interactive diagram](/diagrams/models/dexined/b-edge.html)