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Mask2former Swin Large Apple Dms Run4 | ST Studio
Resources / Mask2former Swin Large Apple Dms Run4 Mask2former Swin Large Apple Dms Run4 This model is a fine-tuned version of facebook/mask2former-swin-large-ade-semantic on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set: Mean Iou: 0.4591 Mean Accuracy:...
This model is a fine-tuned version of facebook/mask2former-swin-large-ade-semantic on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set: Mean Iou: 0.4591 Mean Accuracy:...
Kind Other Base model facebook/mask2former-swin-large-ade-semantic Version Revision 33d6fde
Model source
Kind Other
File size 826 MB
Base model facebook/mask2former-swin-large-ade-semantic
Version vRevision 33d6fde
Updated Sep 27, 2026
Downloads 11
Tasks Image segmentation
Source Hugging Face
mask2former-swin-large-apple-dms-run4
This model is a fine-tuned version of facebook/mask2former-swin-large-ade-semantic on the AllanK24/apple-dms-materials dataset.
It achieves the following results on the evaluation set:
Model artifacts
1 artifact model.safetensors
safetensors · 826 MB · SHA-256 8919d85aa058…a3f9 · Hugging Face
Download Source excerpts
2 excerpts
Mean Iou: 0.4591
Mean Accuracy: 0.5161
Overall Accuracy: 0.7173
Iou Animal Skin: 0.3109
Accuracy Animal Skin: 0.3977
Iou Bone Teeth Horn: 0.4324
Accuracy Bone Teeth Horn: 0.4782
Iou Brickwork: 0.5801
Accuracy Brickwork: 0.6358
Iou Cardboard: 0.4966
Accuracy Cardboard: 0.5428
Iou Carpet Rug: 0.7349
Accuracy Carpet Rug: 0.7935
Iou Ceiling Tile: 0.7489
Accuracy Ceiling Tile: 0.7823
Iou Ceramic: 0.6562
Accuracy Ceramic: 0.7301
Iou Chalkboard Blackboard: 0.6227
Accuracy Chalkboard Blackboard: 0.6661
Iou Clutter: 0.0381
Accuracy Clutter: 0.0383
Iou Concrete: 0.3728
Accuracy Concrete: 0.4723
Iou Cork Corkboard: 0.0839
Accuracy Cork Corkboard: 0.0844
Iou Engineered Stone: 0.0342
Accuracy Engineered Stone: 0.0353
Iou Fabric Cloth: 0.8062
Accuracy Fabric Cloth: 0.8579
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.5042
Accuracy Fire: 0.5230
Iou Foliage: 0.8522
Accuracy Foliage: 0.9084
Iou Food: 0.8211
Accuracy Food: 0.8988
Iou Fur: 0.7754
Accuracy Fur: 0.8430
Iou Gemstone Quartz: 0.0406
Accuracy Gemstone Quartz: 0.0695
Iou Glass: 0.5968
Accuracy Glass: 0.6608
Iou Hair: 0.7814
Accuracy Hair: 0.8625
Iou Ice: 0.1174
Accuracy Ice: 0.1280
Iou Leather: 0.3807
Accuracy Leather: 0.4262
Iou Liquid Non-water: 0.1361
Accuracy Liquid Non-water: 0.1513
Iou Metal: 0.3657
Accuracy Metal: 0.4471
Iou Mirror: 0.4949
Accuracy Mirror: 0.5409
Iou Paint Plaster Enamel: 0.6501
Accuracy Paint Plaster Enamel: 0.7013
Iou Paper: 0.6232
Accuracy Paper: 0.6883
Iou Pearl: 0.0004
Accuracy Pearl: 0.0004
Iou Photograph Painting: 0.2868
Accuracy Photograph Painting: 0.3323
Iou Plastic Clear: 0.3198
Accuracy Plastic Clear: 0.3979
Iou Plastic Non-clear: 0.3612
Accuracy Plastic Non-clear: 0.4326
Iou Rubber Latex: 0.3327
Accuracy Rubber Latex: 0.3764
Iou Sand: 0.6367
Accuracy Sand: 0.7567
Iou Skin Lips: 0.7983
Accuracy Skin Lips: 0.8790
Iou Sky: 0.9352
Accuracy Sky: 0.9560
Iou Snow: 0.6390
Accuracy Snow: 0.7559
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.4450
Accuracy Soil Mud: 0.5742
Iou Sponge: 0.0175
Accuracy Sponge: 0.0182
Iou Stone Natural: 0.5482
Accuracy Stone Natural: 0.6817
Iou Stone Polished: 0.2297
Accuracy Stone Polished: 0.2775
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.6461
Accuracy Tile: 0.7098
Iou Wallpaper: 0.4726
Accuracy Wallpaper: 0.6015
Iou Water: 0.8351
Accuracy Water: 0.9155
Iou Wax: 0.6406
Accuracy Wax: 0.7066
Iou Whiteboard: 0.6796
Accuracy Whiteboard: 0.7674
Iou Wicker: 0.4616
Accuracy Wicker: 0.5149
Iou Wood: 0.6358
Accuracy Wood: 0.6738
Iou Wood Tree: 0.3828
Accuracy Wood Tree: 0.5080
Iou Asphalt: 0.5091
Accuracy Asphalt: 0.6385
Loss: 287.8637
Eval Runtime: 38.282
Eval Samples Per Second: 30.928
Eval Steps Per Second: 15.464
Model description
Intended uses & limitations
Training and evaluation data
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: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 50
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 311.9153 1.7045 150 0.2421 0.2905 0.6995 0.0 0.0 0.0020 0.0020 0.1554 0.1673 0.2775 0.2864 0.6874 0.8297 0.7511 0.7886 0.5468 0.6226 0.0 0.0 0.0 0.0 0.0 0.0
---
library_name: transformers
license: other
base_model: facebook/mask2former-swin-large-ade-semantic
tags:
- vision
- image-segmentation
- mask2former
- material-segmentation
- generated_from_trainer
model-index:
- name: mask2former-swin-large-apple-dms-run4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mask2former-swin-large-apple-dms-run4
This model is a fine-tuned version of [facebook/mask2former-swin-large-ade-semantic](https://huggingface.co/facebook/mask2former-swin-large-ade-semantic) on the AllanK24/apple-dms-materials dataset.
It achieves the following results on the evaluation set:
- Mean Iou: 0.4591
- Mean Accuracy: 0.5161
- Overall Accuracy: 0.7173
- Iou Animal Skin: 0.3109
- Accuracy Animal Skin: 0.3977
- Iou Bone Teeth Horn: 0.4324
- Accuracy Bone Teeth Horn: 0.4782
- Iou Brickwork: 0.5801
- Accuracy Brickwork: 0.6358
- Iou Cardboard: 0.4966
- Accuracy Cardboard: 0.5428
- Iou Carpet Rug: 0.7349
- Accuracy Carpet Rug: 0.7935
- Iou Ceiling Tile: 0.7489
- Accuracy Ceiling Tile: 0.7823
- Iou Ceramic: 0.6562
- Accuracy Ceramic: 0.7301
- Iou Chalkboard Blackboard: 0.6227
- Accuracy Chalkboard Blackboard: 0.6661
- Iou Clutter: 0.0381
- Accuracy Clutter: 0.0383
- Iou Concrete: 0.3728
- Accuracy Concrete: 0.4723
- Iou Cork Corkboard: 0.0839
- Accuracy Cork Corkboard: 0.0844
- Iou Engineered Stone: 0.0342
- Accuracy Engineered Stone: 0.0353
- Iou Fabric Cloth: 0.8062
- Accuracy Fabric Cloth: 0.8579
- Iou Fiberglass Wool: 0.0
- Accuracy Fiberglass Wool: 0.0
- Iou Fire: 0.5042
- Accuracy Fire: 0.5230
- Iou Foliage: 0.8522
- Accuracy Foliage: 0.9084
- Iou Food: 0.8211
- Accuracy Food: 0.8988
- Iou Fur: 0.7754
- Accuracy Fur: 0.8430
- Iou Gemstone Quartz: 0.0406
- Accuracy Gemstone Quartz: 0.0695
- Iou Glass: 0.5968
- Accuracy Glass: 0.6608
- Iou Hair: 0.7814
- Accuracy Hair: 0.8625
- Iou Ice: 0.1174
- Accuracy Ice: 0.1280
- Iou Leather: 0.3807
- Accuracy Leather: 0.4262
- Iou Liquid Non-water: 0.1361
- Accuracy Liquid Non-water: 0.1513
- Iou Metal: 0.3657
- Accuracy Metal: 0.4471
- Iou Mirror: 0.4949
- Accuracy Mirror: 0.5409
- Iou Paint Plaster Enamel: 0.6501
- Accuracy Paint Plaster Enamel: 0.7013
- Iou Paper: 0.6232
- Accuracy Paper: 0.6883
- Iou Pearl: 0.0004
- Accuracy Pearl: 0.0004
- Io...
Source context: 11 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/mask2former-swin-large-apple-dms-run4
Accuracy Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
311.9153 1.7045 150 0.2421 0.2905 0.6995 0.0 0.0 0.0020 0.0020 0.1554 0.1673 0.2775 0.2864 0.6874 0.8297 0.7511 0.7886 0.5468 0.6226 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7802 0.8652 0.0 0.0 0.0 0.0 0.7861 0.8544 0.2189 0.2540 0.2725 0.9172 0.0 0.0 0.5744 0.6602 0.7212 0.8057 0.0 0.0 0.0297 0.0302 0.0 0.0 0.1880 0.2082 0.4791 0.5482 0.7035 0.8205 0.4991 0.5488 0.0 0.0 0.3058 0.5425 0.0780 0.0975 0.1653 0.1773 0.0072 ...
