EoMT

• Original code: https://github.com/huggingface/transformers at c4c4605f9618
• Code licence: Apache-2.0 (permissive); files:
• Weights licence: MIT
  • eomt-l-dinov3-ade20k: MIT AND LicenseRef-DINOv3-License
• Licence evidence: https://huggingface.co/tue-mps/ade20k_semantic_eomt_large_512/blob/aeb262f5a0bc609236d27f1277c3173ce57aeb27/README.md
• Training data: LVD-142M (DINOv2's curated web-image set, not released), ADE20K (SceneParsing, 150 classes)
  • eomt-l-dinov3-ade20k: ADE20K (SceneParsing, 150 classes) (fine-tuned from Meta's DINOv3 ViT-L/16 (pre-trained on LVD-1689M, not in datasets.toml; the DINOv3 weights are under the DINOv3 License); the EoMT model card says MIT)
• Licence note: The eomt-l-dinov3-ade20k checkpoint is fine-tuned from DINOv3, so it also carries the DINOv3 License (redistribution must include its text); that licence does not exclude commercial use.
• Kernel libraries: fastbb, seg_up
• Variants: eomt-l-dinov2-ade20k, eomt-l-dinov3-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): eomt-l-dinov2-ade20k 3000x4000 (14.5 GiB peak allocated, 14.7 reserved); eomt-l-dinov3-ade20k 3000x4000 (14.5 GiB peak allocated, 14.7 reserved)
• eomt-l-dinov2-ade20k 1024x2048, first call: portable + torch.compile 17 s (cold Inductor cache), TensorRT fp16 53 s (ONNX + engine).
• eomt-l-dinov2-ade20k original 1024x2048: torch.autocast(fp16) around the forward
• eomt-l-dinov3-ade20k original 1024x2048: torch.autocast(fp16) around the forward
• eomt-l-dinov2-ade20k load / first call: original 4.8 s / 0.537 s (512x512), portable 3.13 s / 0.053 s, kernels 3.27† s / 0.036† s
• eomt-l-dinov3-ade20k load / first call: original 1.83 s / 0.065 s (512x512), portable 3.2 s / 0.078 s, kernels 3.24† s / 0.039† 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| <= 0.0002 and p99 <= 0.003 (absolute error of the output logits).