Instructions to use ArashNasr/ai2thor-kitchen-perception-floorplan15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ArashNasr/ai2thor-kitchen-perception-floorplan15 with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("ArashNasr/ai2thor-kitchen-perception-floorplan15", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
AI2-THOR kitchen perception models: FloorPlan15
Status: public release, part of MISE (project page; an English paper is in preparation). Part of an MSc thesis (University of Tehran): an LLM + RAG embodied agent that plans L1 atomic / L2 sequential / L3 abstract kitchen commands in AI2-THOR, with an honest perception stack (no simulator ground truth at test time).
FloorPlan15 is a held-out replication kitchen. Its detectors were initialized from the frozen FloorPlan1 models (A38) and trained on its own capture; the numbers below are its registered TEST evaluation (2026-10-21; each step run once, sealed holdout opened once).
Models
| File | Role |
|---|---|
FloorPlan15.yolo26s.pt |
primary detector, YOLO26s, 49 type-only classes, every frame |
FloorPlan15.amb26m.pt |
look-alike specialist, YOLO26m (Fork, Spoon, ButterKnife, Knife, Spatula, SaltShaker, PepperShaker, Plate, Pot, Pan); replaces the primary's predictions for those classes only while the agent holds an object up (gated) |
FloorPlan15.yolo26s.control.pt |
control: YOLO26s fine-tuned on primary + specialist data (the comparison the specialist is tested against) |
FloorPlan15.state_*.pt |
7 state classifiers (broken, cooked, dirty, filled, on, open, sliced), YOLO11s-cls |
FloorPlan15.yolo26s_coco.pt |
registered sensitivity only: the same primary trained from the COCO checkpoint (never deployed) |
SHA256SUMS lists the exact deployed bytes (they match the kitchen's frozen record).
Results
| Measure | Value |
|---|---|
| primary detector (YOLO26s), TEST / CLOSE-TEST / LIVE coverage mAP50 | 0.934 / 0.943 / 0.984 |
| primary detector, TEST / CLOSE-TEST / LIVE coverage mAP50-95 | 0.854 / 0.885 / 0.861 |
| LIVE realistic set mAP50 / mAP50-95 (150 poses, only 123 ground-truth boxes) | 0.627 / 0.551 |
| sealed holdout (opened once): held-target naming success, primary / gated (with specialist) / control | 0.662 / 0.948 / 0.849 |
| gated minus primary; gated minus control (paired episode bootstrap, 95 % CI) | +28.6 pp [+23.9, +32.8]; +9.9 pp [+6.0, +14.6] |
| COCO-start sensitivity: TEST mAP50-95 COCO minus Kitchen-1 start | -0.5 pp [-0.8, -0.1] (the deployed model is the Kitchen-1 start) |
Every number belongs to this kitchen's own adapted detectors and is never pooled across kitchens.
Licence and credits
AGPL-3.0 (fine-tuned from Ultralytics YOLO models). Training images rendered with AI2-THOR (Apache-2.0).
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