OneFormer

• Original code: https://github.com/huggingface/transformers at c4c4605f9618
• Code licence: Apache-2.0 (permissive); files:
• Weights licence: MIT
• Licence evidence: https://huggingface.co/shi-labs/oneformer_ade20k_swin_large/blob/4a5bac8e64f82681a12db2e151a4c2f4ce6092b2/README.md
• Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ADE20K (SceneParsing, 150 classes)
• Kernel libraries: fvm_seg, seg_up
• Variants: oneformer-swinl-ade20k
• Outputs: logits; label_set: ade20k-150
• Input size: any H x W >= 1, resized or padded inside the model
• Tested up to (portable, fp16, RTX 5060 Ti 16 GB): oneformer-swinl-ade20k 1988x2880 (8.4 GiB peak allocated, 14.9 reserved)
• oneformer-swinl-ade20k 1024x2048, first call: TensorRT fp16 140 s (ONNX + engine).
• oneformer-swinl-ade20k original 1024x2048: torch.autocast(fp16) around the forward
• oneformer-swinl-ade20k load / first call: original 5.08 s / 0.805 s (512x512), portable 4.1 s / 0.147† s, kernels 3.27† s / 0.109† s
• † measured in the speed sweep after the portable load (or an earlier size) in the same process: file cache and kernels are warm, so the time is lower than a fresh start.
• Protocol, first call, 3 warm-up, peak reset, median of 3 rounds x median of 5 (tools/speed_check.py), synthetic CUDA input (bench_inputs); peak = max over the first call and the timed calls

Measured

Each image at its own size, argmax of the logits. Original = the original code named under Details, same checkpoint and protocol.

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 939adfe8, 2026-11-06.

Parity

CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.001 and p99 <= 0.01 (absolute error of the output logits).