mask2former-swin-base-apple-dms-run1

AllanK24/mask2former-swin-base-apple-dms-run1

mask2former-swin-base-apple-dms-run1 is a image segmentation model from AllanK24. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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Base models

facebook/mask2former-swin-base-ade-semantic

Model card

From the Hugging Face model README

mask2former-swin-base-apple-dms-run1

This model is a fine-tuned version of facebook/mask2former-swin-base-ade-semantic on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set:

Mean Iou: 0.0129
Mean Accuracy: 0.0190
Overall Accuracy: 0.2006
Iou Animal Skin: 0.0
Accuracy Animal Skin: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Bone Teeth Horn: 0.0
Iou Brickwork: 0.0
Accuracy Brickwork: 0.0
Iou Cardboard: 0.0
Accuracy Cardboard: 0.0
Iou Carpet Rug: 0.0000
Accuracy Carpet Rug: 0.0000
Iou Ceiling Tile: 0.0
Accuracy Ceiling Tile: 0.0
Iou Ceramic: 0.0
Accuracy Ceramic: 0.0
Iou Chalkboard Blackboard: 0.0
Accuracy Chalkboard Blackboard: 0.0
Iou Clutter: 0.0
Accuracy Clutter: 0.0
Iou Concrete: 0.0
Accuracy Concrete: 0.0
Iou Cork Corkboard: 0.0
Accuracy Cork Corkboard: 0.0
Iou Engineered Stone: 0.0
Accuracy Engineered Stone: 0.0
Iou Fabric Cloth: 0.0045
Accuracy Fabric Cloth: 0.0045
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.0
Accuracy Fire: 0.0
Iou Foliage: 0.0
Accuracy Foliage: 0.0
Iou Food: 0.0
Accuracy Food: 0.0
Iou Fur: 0.0
Accuracy Fur: 0.0
Iou Gemstone Quartz: 0.0
Accuracy Gemstone Quartz: 0.0
Iou Glass: 0.0593
Accuracy Glass: 0.0676
Iou Hair: 0.0685
Accuracy Hair: 0.0885
Iou Ice: 0.0
Accuracy Ice: 0.0
Iou Leather: 0.0
Accuracy Leather: 0.0
Iou Liquid Non-water: 0.0
Accuracy Liquid Non-water: 0.0
Iou Metal: 0.0036
Accuracy Metal: 0.0038
Iou Mirror: 0.0
Accuracy Mirror: 0.0
Iou Paint Plaster Enamel: 0.4088
Accuracy Paint Plaster Enamel: 0.6439
Iou Paper: 0.0141
Accuracy Paper: 0.0153
Iou Pearl: 0.0
Accuracy Pearl: 0.0
Iou Photograph Painting: 0.0
Accuracy Photograph Painting: 0.0
Iou Plastic Clear: 0.0
Accuracy Plastic Clear: 0.0
Iou Plastic Non-clear: 0.0000
Accuracy Plastic Non-clear: 0.0000
Iou Rubber Latex: 0.0
Accuracy Rubber Latex: 0.0
Iou Sand: 0.0
Accuracy Sand: 0.0
Iou Skin Lips: 0.1127
Accuracy Skin Lips: 0.1643
Iou Sky: 0.0
Accuracy Sky: 0.0
Iou Snow: 0.0
Accuracy Snow: 0.0
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.0
Accuracy Soil Mud: 0.0
Iou Sponge: 0.0
Accuracy Sponge: 0.0
Iou Stone Natural: 0.0
Accuracy Stone Natural: 0.0
Iou Stone Polished: 0.0
Accuracy Stone Polished: 0.0
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.0
Accuracy Tile: 0.0
Iou Wallpaper: 0.0
Accuracy Wallpaper: 0.0
Iou Water: 0.0
Accuracy Water: 0.0
Iou Wax: 0.0
Accuracy Wax: 0.0
Iou Whiteboard: 0.0
Accuracy Whiteboard: 0.0
Iou Wicker: 0.0
Accuracy Wicker: 0.0
Iou Wood: 0.0001
Accuracy Wood: 0.0001
Iou Wood Tree: 0.0
Accuracy Wood Tree: 0.0
Iou Asphalt: 0.0
Accuracy Asphalt: 0.0
Loss: 411.6706
Eval Runtime: 41.6536
Eval Samples Per Second: 28.425
Eval Steps Per Second: 14.212

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 256
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Mean Iou	Mean Accuracy	Overall Accuracy	Iou Animal Skin	Accuracy Animal Skin	Iou Bone Teeth Horn	Accuracy Bone Teeth Horn	Iou Brickwork	Accuracy Brickwork	Iou Cardboard	Accuracy Cardboard	Iou Carpet Rug	Accuracy Carpet Rug	Iou Ceiling Tile	Accuracy Ceiling Tile	Iou Ceramic	Accuracy Ceramic	Iou Chalkboard Blackboard	Accuracy Chalkboard Blackboard	Iou Clutter	Accuracy Clutter	Iou Concrete	Accuracy Concrete	Iou Cork Corkboard	Accuracy Cork Corkboard	Iou Engineered Stone	Accuracy Engineered Stone	Iou Fabric Cloth	Accuracy Fabric Cloth	Iou Fiberglass Wool	Accuracy Fiberglass Wool	Iou Fire	Accuracy Fire	Iou Foliage	Accuracy Foliage	Iou Food	Accuracy Food	Iou Fur	Accuracy Fur	Iou Gemstone Quartz	Accuracy Gemstone Quartz	Iou Glass	Accuracy Glass	Iou Hair	Accuracy Hair	Iou Ice	Accuracy Ice	Iou Leather	Accuracy Leather	Iou Liquid Non-water	Accuracy Liquid Non-water	Iou Metal	Accuracy Metal	Iou Mirror	Accuracy Mirror	Iou Paint Plaster Enamel	Accuracy Paint Plaster Enamel	Iou Paper	Accuracy Paper	Iou Pearl	Accuracy Pearl	Iou Photograph Painting	Accuracy Photograph Painting	Iou Plastic Clear	Accuracy Plastic Clear	Iou Plastic Non-clear	Accuracy Plastic Non-clear	Iou Rubber Latex	Accuracy Rubber Latex	Iou Sand	Accuracy Sand	Iou Skin Lips	Accuracy Skin Lips	Iou Sky	Accuracy Sky	Iou Snow	Accuracy Snow	Iou Soap	Accuracy Soap	Iou Soil Mud	Accuracy Soil Mud	Iou Sponge	Accuracy Sponge	Iou Stone Natural	Accuracy Stone Natural	Iou Stone Polished	Accuracy Stone Polished	Iou Styrofoam	Accuracy Styrofoam	Iou Tile	Accuracy Tile	Iou Wallpaper	Accuracy Wallpaper	Iou Water	Accuracy Water	Iou Wax	Accuracy Wax	Iou Whiteboard	Accuracy Whiteboard	Iou Wicker	Accuracy Wicker	Iou Wood	Accuracy Wood	Iou Wood Tree	Accuracy Wood Tree	Iou Asphalt	Accuracy Asphalt	Validation Loss	Eval Runtime	Eval Samples Per Second	Eval Steps Per Second
475.7108	0.5682	50	0.0129	0.0190	0.2006	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0045	0.0045	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0593	0.0676	0.0685	0.0885	0.0	0.0	0.0	0.0	0.0	0.0	0.0036	0.0038	0.0	0.0	0.4088	0.6439	0.0141	0.0153	0.0	0.0	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.0	0.0	0.1127	0.1643	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0001	0.0	0.0	0.0	0.0	411.6706	41.6536	28.425	14.212
475.7108	0.5682	50	0.0129	0.0190	0.2006	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0045	0.0045	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0593	0.0676	0.0685	0.0885	0.0	0.0	0.0	0.0	0.0	0.0	0.0036	0.0038	0.0	0.0	0.4088	0.6439	0.0141	0.0153	0.0	0.0	0.0	0.0	0.0	0.0	0.0000	0.0000	0.0	0.0	0.0	0.0	0.1127	0.1643	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0001	0.0001	0.0	0.0	0.0	0.0	411.6706	41.6536	28.425	14.212

Framework versions

Transformers 5.0.0
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Task: Image Segmentation
Library: transformers
Type: mask2former
License: other
Created: Feb 8, 2026
Updated: Feb 8, 2026