Mask2Former

• Original code: https://github.com/huggingface/transformers at c4c4605f9618
• Code licence: Apache-2.0 (permissive); files:
• Weights licence: CC-BY-NC-4.0
• Licence evidence: https://github.com/facebookresearch/Mask2Former/blob/9b0651c6c1d5b3af2e6da0589b719c514ec0d69a/MODEL_ZOO.md#license
• Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ADE20K (SceneParsing, 150 classes)
• Kernel libraries: fvm_seg, seg_up
• Variants: mask2former-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): mask2former-swinl-ade20k 3000x4000 (14.1 GiB peak allocated, 15.1 reserved)
• mask2former-swinl-ade20k 1024x2048, first call: TensorRT fp16 129 s (ONNX + engine).
• mask2former-swinl-ade20k original 1024x2048: torch.autocast(fp16) around the forward
• mask2former-swinl-ade20k load / first call: original 4.86 s / 0.763 s (512x512), portable 3.03 s / 0.127 s, kernels 3.17† s / 0.069† 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).