phungpx/mask2former-swin-base-cubicassa

Mask2Former — CubiCasa5K Instance Segmentation

Fine-tuned version of facebook/mask2former-swin-base-coco-instance on the phungpx/cubicassa5k-coco dataset for instance segmentation of architectural floor-plan elements.

Classes

Predicts 1 class(es): wall.

Evaluation — COCO mask AP (validation split)

Metric	Value
mask AP @[IoU=0.50:0.95]	0.263
mask AP @[IoU=0.50]	0.620
mask AP @[IoU=0.75]	0.179

Prediction samples

Columns: input floor-plan, ground-truth instances, and model prediction (mask fill + boxes; predictions also show confidence scores).

Training hyperparameters

Hyperparameter	Value
base model	facebook/mask2former-swin-base-coco-instance
image size	640
batch size	2
epochs	50
learning rate	1e-05
weight decay	0.05
lr scheduler	cosine (warmup ratio 0.05)
optimizer	AdamW
mixed precision	fp16
seed	42

Usage

import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation

processor = AutoImageProcessor.from_pretrained("phungpx/mask2former-swin-base-cubicassa")
model = Mask2FormerForUniversalSegmentation.from_pretrained("phungpx/mask2former-swin-base-cubicassa")
model.eval()

image = Image.open("floorplan.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

results = processor.post_process_instance_segmentation(
    outputs, target_sizes=[image.size[::-1]], threshold=0.5
)[0]

Fine-tuned with the 🤗 Transformers Trainer API.