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Mask2former Swin Large Apple Dms Run3 | ST Studio
Ressources / Mask2former Swin Large Apple Dms Run3 Mask2former Swin Large Apple Dms Run3 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: 0.4468 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: 0.4468 Mean Accuracy:...
Type Other Modèle de base facebook/mask2former-swin-large-ade-semantic Version Revision 04c6d80
Source du modèle
Type Other
Taille du fichier 826 MB
Modèle de base facebook/mask2former-swin-large-ade-semantic
Version vRevision 04c6d80
Mis à jour 27 sept. 2026
Téléchargements 13
Tâches Segmentation d'image
Source Hugging Face
mask2former-swin-large-apple-dms-run3
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:
Artefacts du modèle
1 artefact model.safetensors
safetensors · 826 MB · SHA-256 7f6be9e952fc…2483 · Hugging Face
Télécharger Extraits de sources
2 extraits
Mean Iou: 0.4468
Mean Accuracy: 0.4980
Overall Accuracy: 0.7620
Iou Animal Skin: 0.2782
Accuracy Animal Skin: 0.3669
Iou Bone Teeth Horn: 0.3516
Accuracy Bone Teeth Horn: 0.3806
Iou Brickwork: 0.6013
Accuracy Brickwork: 0.6532
Iou Cardboard: 0.5444
Accuracy Cardboard: 0.5949
Iou Carpet Rug: 0.7696
Accuracy Carpet Rug: 0.8371
Iou Ceiling Tile: 0.7731
Accuracy Ceiling Tile: 0.8172
Iou Ceramic: 0.6828
Accuracy Ceramic: 0.7668
Iou Chalkboard Blackboard: 0.6688
Accuracy Chalkboard Blackboard: 0.7109
Iou Clutter: 0.0
Accuracy Clutter: 0.0
Iou Concrete: 0.3679
Accuracy Concrete: 0.4470
Iou Cork Corkboard: 0.0
Accuracy Cork Corkboard: 0.0
Iou Engineered Stone: 0.0
Accuracy Engineered Stone: 0.0
Iou Fabric Cloth: 0.8207
Accuracy Fabric Cloth: 0.8714
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.5244
Accuracy Fire: 0.5419
Iou Foliage: 0.8612
Accuracy Foliage: 0.9157
Iou Food: 0.8339
Accuracy Food: 0.9069
Iou Fur: 0.8275
Accuracy Fur: 0.8946
Iou Gemstone Quartz: 0.0058
Accuracy Gemstone Quartz: 0.0063
Iou Glass: 0.6220
Accuracy Glass: 0.6865
Iou Hair: 0.7757
Accuracy Hair: 0.8573
Iou Ice: 0.0
Accuracy Ice: 0.0
Iou Leather: 0.4593
Accuracy Leather: 0.5184
Iou Liquid Non-water: 0.0086
Accuracy Liquid Non-water: 0.0088
Iou Metal: 0.3786
Accuracy Metal: 0.4462
Iou Mirror: 0.5531
Accuracy Mirror: 0.6207
Iou Paint Plaster Enamel: 0.7352
Accuracy Paint Plaster Enamel: 0.7924
Iou Paper: 0.6283
Accuracy Paper: 0.7011
Iou Pearl: 0.0
Accuracy Pearl: 0.0
Iou Photograph Painting: 0.3018
Accuracy Photograph Painting: 0.3438
Iou Plastic Clear: 0.3244
Accuracy Plastic Clear: 0.3767
Iou Plastic Non-clear: 0.3700
Accuracy Plastic Non-clear: 0.4420
Iou Rubber Latex: 0.3300
Accuracy Rubber Latex: 0.3667
Iou Sand: 0.3527
Accuracy Sand: 0.3737
Iou Skin Lips: 0.7911
Accuracy Skin Lips: 0.8780
Iou Sky: 0.9400
Accuracy Sky: 0.9603
Iou Snow: 0.6050
Accuracy Snow: 0.7399
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.4265
Accuracy Soil Mud: 0.6304
Iou Sponge: 0.0
Accuracy Sponge: 0.0
Iou Stone Natural: 0.5347
Accuracy Stone Natural: 0.6035
Iou Stone Polished: 0.1553
Accuracy Stone Polished: 0.1618
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.6659
Accuracy Tile: 0.7333
Iou Wallpaper: 0.4696
Accuracy Wallpaper: 0.5588
Iou Water: 0.8496
Accuracy Water: 0.9329
Iou Wax: 0.2270
Accuracy Wax: 0.2572
Iou Whiteboard: 0.6993
Accuracy Whiteboard: 0.7630
Iou Wicker: 0.4585
Accuracy Wicker: 0.4913
Iou Wood: 0.7335
Accuracy Wood: 0.7927
Iou Wood Tree: 0.4152
Accuracy Wood Tree: 0.4921
Iou Asphalt: 0.5099
Accuracy Asphalt: 0.6563
Loss: 258.2648
Eval Runtime: 39.2199
Eval Samples Per Second: 30.189
Eval Steps Per Second: 15.094
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: 20
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 299.5840 1.7045 150 0.2567 0.2970 0.6983 0.0000 0.0000 0.0222 0.0224 0.3400 0.4233 0.3128 0.3198 0.7021 0.8554 0.7589 0.8491 0.6248 0.6913 0.0036 0.0036 0.0 0.0 0.0111 0.0112
---
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-run3
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-run3
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: 0.4468
- Mean Accuracy: 0.4980
- Overall Accuracy: 0.7620
- Iou Animal Skin: 0.2782
- Accuracy Animal Skin: 0.3669
- Iou Bone Teeth Horn: 0.3516
- Accuracy Bone Teeth Horn: 0.3806
- Iou Brickwork: 0.6013
- Accuracy Brickwork: 0.6532
- Iou Cardboard: 0.5444
- Accuracy Cardboard: 0.5949
- Iou Carpet Rug: 0.7696
- Accuracy Carpet Rug: 0.8371
- Iou Ceiling Tile: 0.7731
- Accuracy Ceiling Tile: 0.8172
- Iou Ceramic: 0.6828
- Accuracy Ceramic: 0.7668
- Iou Chalkboard Blackboard: 0.6688
- Accuracy Chalkboard Blackboard: 0.7109
- Iou Clutter: 0.0
- Accuracy Clutter: 0.0
- Iou Concrete: 0.3679
- Accuracy Concrete: 0.4470
- Iou Cork Corkboard: 0.0
- Accuracy Cork Corkboard: 0.0
- Iou Engineered Stone: 0.0
- Accuracy Engineered Stone: 0.0
- Iou Fabric Cloth: 0.8207
- Accuracy Fabric Cloth: 0.8714
- Iou Fiberglass Wool: 0.0
- Accuracy Fiberglass Wool: 0.0
- Iou Fire: 0.5244
- Accuracy Fire: 0.5419
- Iou Foliage: 0.8612
- Accuracy Foliage: 0.9157
- Iou Food: 0.8339
- Accuracy Food: 0.9069
- Iou Fur: 0.8275
- Accuracy Fur: 0.8946
- Iou Gemstone Quartz: 0.0058
- Accuracy Gemstone Quartz: 0.0063
- Iou Glass: 0.6220
- Accuracy Glass: 0.6865
- Iou Hair: 0.7757
- Accuracy Hair: 0.8573
- Iou Ice: 0.0
- Accuracy Ice: 0.0
- Iou Leather: 0.4593
- Accuracy Leather: 0.5184
- Iou Liquid Non-water: 0.0086
- Accuracy Liquid Non-water: 0.0088
- Iou Metal: 0.3786
- Accuracy Metal: 0.4462
- Iou Mirror: 0.5531
- Accuracy Mirror: 0.6207
- Iou Paint Plaster Enamel: 0.7352
- Accuracy Paint Plaster Enamel: 0.7924
- Iou Paper: 0.6283
- Accuracy Paper: 0.7011
- Iou Pearl: 0.0
- Accuracy Pearl: 0.0
- Iou Photograph Painting: 0.3018...
Source context: 13 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/mask2former-swin-large-apple-dms-run3
Accuracy Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
299.5840 1.7045 150 0.2567 0.2970 0.6983 0.0000 0.0000 0.0222 0.0224 0.3400 0.4233 0.3128 0.3198 0.7021 0.8554 0.7589 0.8491 0.6248 0.6913 0.0036 0.0036 0.0 0.0 0.0111 0.0112 0.0 0.0 0.0 0.0 0.7983 0.8824 0.0 0.0 0.0 0.0 0.8305 0.9335 0.1855 0.1919 0.4690 0.8489 0.0 0.0 0.5660 0.6470 0.7348 0.8313 0.0 0.0 0.1573 0.1615 0.0 0.0 0.1541 0.1582 0.5614 0.7057 0.7090 0.7997 0.5197 0.5864 0.0 0.0 0.2181 0.2485 0.1523 0.1860 0.1909 0.2053 0.0119 0.0119 0.0 ...
