Mask2former Swin Base Cubicassa

--- base_model: facebook/mask2former-swin-base-coco-instance datasets: - phungpx/cubicassa5k-coco library_name: transformers license: other metrics: - mAP pipeline_tag: image-segmentation tags: - mask2former - instance-segmentation - vision - cubicasa5k - floor-plan --- # Mask2Former — CubiCasa5K Instance Segmentation Fine-tuned version of [`facebook/mask2former-swin-base-coco-instance`](https://huggingface.co/facebook/mask2former-swin-base-coco-instance) on the [`phungpx/cubicassa5k-coco`](https://huggingface.co/datasets/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 ```python 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.