FixedSeed/lattice-vision

Lattice vision models

ONNX files that Lattice downloads on first use for its Matte and Body Pose nodes. They run in onnxruntime-web on WebGPU, with every node on the GPU. The weights are the original authors', under their licences; only the format changed (see below).

File	Model	Authors	Licence
birefnet-lite-matting-512-fp16.onnx, -768-, -1024-	BiRefNet_lite-matting (epoch 110), one fixed square input size per file	Peng Zheng et al., ZhengPeng7/BiRefNet	MIT (LICENSE-BiRefNet-MIT.txt)
rtmw-m-256x192-fp16.onnx	RTMW-m (rtmw-dw-l-m_simcc-cocktail14, 256x192): 133 COCO-WholeBody points	OpenMMLab, open-mmlab/mmpose	Apache-2.0 (LICENSE-mmpose-Apache-2.0.txt)
rtmw-l-384x288-fp16.onnx	RTMW-l (rtmw-dw-x-l_simcc-cocktail14, 384x288)	OpenMMLab	Apache-2.0
yolox-tiny-humanart.onnx	YOLOX-tiny trained on Human-Art (person boxes, NMS included), OpenMMLab's end2end.onnx unchanged	OpenMMLab (YOLOX: Megvii)	Apache-2.0

What was changed

• BiRefNet: exported from the PyTorch weights at opset 17 with each torchvision deform_conv2d replaced by one GridSample plus a MatMul (the same maths, within 5e-5 of torchvision's op; the whole model within 3e-5 on its logits), so no int64 index arithmetic falls back to the CPU on WebGPU. Then onnxslim, fp16 weights with float32 inputs and outputs, and the backbone's weights, which the export writes once per call (BiRefNet runs its backbone at full and half scale), stored once. scripts/export_birefnet_rt.py and scripts/dedupe.py make them. Input input_image (1, 3, S, S), RGB 0..1 normalized with ImageNet's mean and std; output output_image, logits (apply a sigmoid). Accuracy on 100 P3M-500-NP portraits (MSE x 1e-3): 5.10 at 512, 3.60 at 768.
• RTMW: fp16 weights with float32 inputs and outputs (scripts/convert_rtmw.py); keypoints within 0.08 px of the float32 model in its crop. Input (N, 3, H, W) RGB normalized with mean 123.675, 116.28, 103.53 and std 58.395, 57.12, 57.375 (0..255 scale), a person's box grown 1.25 times at the model's aspect; outputs SimCC simcc_x (N, 133, 2W) and simcc_y (N, 133, 2H): argmax / 2 is the point in the crop.
• YOLOX-tiny: unchanged. Input (1, 3, 416, 416) BGR 0..255, the picture scaled into the top left and grey (114) around; outputs dets (1, K, 5: x1, y1, x2, y2, score) and labels (1, K; 0 = person).