TwinLiteNet8

--- license: apache-2.0 language: [en] tags: [semantic-segmentation, twinlitenet, agriculture, orchard, real-time, edge-deployment, jetson] pipeline_tag: image-segmentation --- # TwinLiteNet8 — Real-time orchard segmentation for edge devices A **0.44 M-parameter** semantic-segmentation model adapted from [TwinLiteNet](https://github.com/chequanghuy/TwinLiteNet) for **7-class apple orchard scenes**, designed to run **>30 FPS on Jetson-class hardware** for robotic navigation. Drop-in lightweight alternative to [WEN0256/Segformer85Mv1](https://huggingface.co/WEN0256/Segformer85Mv1) for low-compute deployments. ## Why "7-class" but 8 logit channels? The model is trained to recognize **7 real classes** (`tree`, `ground`, `person`, `sky`, `road`, `mountain`, `building`). The 8th label `background` is **NOT** treated as a real class — pixels that fall outside any labeled object are simply masked out of the loss (`ignore_index=255`). The 8th logit channel exists only to keep the architecture identical to the original TwinLiteNet shape; it is never trained and is forced to `-inf` before `argmax` at inference, so the model never outputs `background`. This matches what you usually want from a robot's perception stack: "tell me what you DO recognize", not "tell me you don't know". ## Performance (no data leakage, temporal split val, fair apples-to-apples) | Metric | TwinLiteNet8 | Segformer-b5 (85 M) | Δ vs Segformer | |---|---|---|---| | Tree IoU | **0.872** | 0.742 | **+13 pp** ⭐ | | Ground IoU | **0.916** | 0.851 | **+6.5 pp** | | Person IoU | 0.441 | 0.72 | -28 pp | | Sky IoU | 0.835 | 0.77 | +6 pp | | Road IoU | 0.745 | 0.80 | -5 pp | | Mountain IoU | 0.592 | 0.44 | +15 pp | | Building IoU | 0.555 | 0.71 | -16 pp | | **mIoU (7 classes)** | **0.708** | 0.714 | -0.6 pp | | Model size | **1.8 MB** | 339 MB | **188× smaller** | | Params | **0.437 M** | 85 M | **194× fewer** | (Segformer numbers come from `WEN0256/Segformer85Mv1`. Both models tested on the same 155-frame temporal-split val from the original orchard recording, with the same "background pixels excluded" protocol so the IoUs are directly comparable.) **Headline:** TwinLiteNet8 *matches* Segformer-b5 in overall mIoU (0.708 vs 0.714, within noise) and *beats it* on the two classes that matter most for orchard navigation (`tree`, `ground`), while being ~200× smaller and ~10× faster on edge device...

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