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Mask2former Swin Large Apple Dms Run1 | ST Studio
Ressources / Mask2former Swin Large Apple Dms Run1 Mask2former Swin Large Apple Dms Run1 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: 0.3738 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: 0.3738 Mean Accuracy:...
Type Other Modèle de base facebook/mask2former-swin-large-ade-semantic Version Revision f9e3c36
Source du modèle
Type Other
Taille du fichier 826 MB
Modèle de base facebook/mask2former-swin-large-ade-semantic
Version vRevision f9e3c36
Mis à jour 27 sept. 2026
Téléchargements 8
Tâches Segmentation d'image
Source Hugging Face
mask2former-swin-large-apple-dms-run1
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 2601bb74cd56…9225 · Hugging Face
Télécharger Extraits de sources
2 extraits
Mean Iou: 0.3738
Mean Accuracy: 0.4172
Overall Accuracy: 0.7341
Iou Animal Skin: 0.1763
Accuracy Animal Skin: 0.2357
Iou Bone Teeth Horn: 0.0785
Accuracy Bone Teeth Horn: 0.0816
Iou Brickwork: 0.5229
Accuracy Brickwork: 0.5694
Iou Cardboard: 0.5144
Accuracy Cardboard: 0.5491
Iou Carpet Rug: 0.7512
Accuracy Carpet Rug: 0.8051
Iou Ceiling Tile: 0.7585
Accuracy Ceiling Tile: 0.8038
Iou Ceramic: 0.6800
Accuracy Ceramic: 0.7569
Iou Chalkboard Blackboard: 0.6079
Accuracy Chalkboard Blackboard: 0.6251
Iou Clutter: 0.0
Accuracy Clutter: 0.0
Iou Concrete: 0.1140
Accuracy Concrete: 0.1274
Iou Cork Corkboard: 0.0
Accuracy Cork Corkboard: 0.0
Iou Engineered Stone: 0.0
Accuracy Engineered Stone: 0.0
Iou Fabric Cloth: 0.8077
Accuracy Fabric Cloth: 0.8577
Iou Fiberglass Wool: 0.0
Accuracy Fiberglass Wool: 0.0
Iou Fire: 0.0
Accuracy Fire: 0.0
Iou Foliage: 0.8477
Accuracy Foliage: 0.9132
Iou Food: 0.7791
Accuracy Food: 0.8473
Iou Fur: 0.7820
Accuracy Fur: 0.8975
Iou Gemstone Quartz: 0.0
Accuracy Gemstone Quartz: 0.0
Iou Glass: 0.6104
Accuracy Glass: 0.6762
Iou Hair: 0.7591
Accuracy Hair: 0.8424
Iou Ice: 0.0
Accuracy Ice: 0.0
Iou Leather: 0.3416
Accuracy Leather: 0.3705
Iou Liquid Non-water: 0.0
Accuracy Liquid Non-water: 0.0
Iou Metal: 0.3338
Accuracy Metal: 0.3816
Iou Mirror: 0.5226
Accuracy Mirror: 0.5838
Iou Paint Plaster Enamel: 0.7258
Accuracy Paint Plaster Enamel: 0.7853
Iou Paper: 0.6264
Accuracy Paper: 0.6989
Iou Pearl: 0.0
Accuracy Pearl: 0.0
Iou Photograph Painting: 0.2530
Accuracy Photograph Painting: 0.2760
Iou Plastic Clear: 0.2795
Accuracy Plastic Clear: 0.3228
Iou Plastic Non-clear: 0.3356
Accuracy Plastic Non-clear: 0.3811
Iou Rubber Latex: 0.2145
Accuracy Rubber Latex: 0.2300
Iou Sand: 0.0
Accuracy Sand: 0.0
Iou Skin Lips: 0.7505
Accuracy Skin Lips: 0.8395
Iou Sky: 0.9236
Accuracy Sky: 0.9515
Iou Snow: 0.0
Accuracy Snow: 0.0
Iou Soap: 0.0
Accuracy Soap: 0.0
Iou Soil Mud: 0.3268
Accuracy Soil Mud: 0.5797
Iou Sponge: 0.0
Accuracy Sponge: 0.0
Iou Stone Natural: 0.4921
Accuracy Stone Natural: 0.6225
Iou Stone Polished: 0.0886
Accuracy Stone Polished: 0.0904
Iou Styrofoam: 0.0
Accuracy Styrofoam: 0.0
Iou Tile: 0.6343
Accuracy Tile: 0.6951
Iou Wallpaper: 0.3391
Accuracy Wallpaper: 0.3889
Iou Water: 0.8342
Accuracy Water: 0.9333
Iou Wax: 0.1469
Accuracy Wax: 0.1538
Iou Whiteboard: 0.6355
Accuracy Whiteboard: 0.6966
Iou Wicker: 0.3825
Accuracy Wicker: 0.4003
Iou Wood: 0.7119
Accuracy Wood: 0.7690
Iou Wood Tree: 0.3640
Accuracy Wood Tree: 0.4130
Iou Asphalt: 0.3848
Accuracy Asphalt: 0.5424
Loss: 261.3355
Eval Runtime: 39.6948
Eval Samples Per Second: 29.828
Eval Steps Per Second: 14.914
Model description
Intended uses & limitations
Training and evaluation data
Training procedure
Training hyperparameters The following hyperparameters were used during training:
learning_rate: 0.0001
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 326.7486 1.7045 150 0.1341 0.1501 0.5649 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4609 0.5478 0.1275 0.1276 0.1313 0.1350 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-run1
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-run1
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: 0.3738
- Mean Accuracy: 0.4172
- Overall Accuracy: 0.7341
- Iou Animal Skin: 0.1763
- Accuracy Animal Skin: 0.2357
- Iou Bone Teeth Horn: 0.0785
- Accuracy Bone Teeth Horn: 0.0816
- Iou Brickwork: 0.5229
- Accuracy Brickwork: 0.5694
- Iou Cardboard: 0.5144
- Accuracy Cardboard: 0.5491
- Iou Carpet Rug: 0.7512
- Accuracy Carpet Rug: 0.8051
- Iou Ceiling Tile: 0.7585
- Accuracy Ceiling Tile: 0.8038
- Iou Ceramic: 0.6800
- Accuracy Ceramic: 0.7569
- Iou Chalkboard Blackboard: 0.6079
- Accuracy Chalkboard Blackboard: 0.6251
- Iou Clutter: 0.0
- Accuracy Clutter: 0.0
- Iou Concrete: 0.1140
- Accuracy Concrete: 0.1274
- Iou Cork Corkboard: 0.0
- Accuracy Cork Corkboard: 0.0
- Iou Engineered Stone: 0.0
- Accuracy Engineered Stone: 0.0
- Iou Fabric Cloth: 0.8077
- Accuracy Fabric Cloth: 0.8577
- Iou Fiberglass Wool: 0.0
- Accuracy Fiberglass Wool: 0.0
- Iou Fire: 0.0
- Accuracy Fire: 0.0
- Iou Foliage: 0.8477
- Accuracy Foliage: 0.9132
- Iou Food: 0.7791
- Accuracy Food: 0.8473
- Iou Fur: 0.7820
- Accuracy Fur: 0.8975
- Iou Gemstone Quartz: 0.0
- Accuracy Gemstone Quartz: 0.0
- Iou Glass: 0.6104
- Accuracy Glass: 0.6762
- Iou Hair: 0.7591
- Accuracy Hair: 0.8424
- Iou Ice: 0.0
- Accuracy Ice: 0.0
- Iou Leather: 0.3416
- Accuracy Leather: 0.3705
- Iou Liquid Non-water: 0.0
- Accuracy Liquid Non-water: 0.0
- Iou Metal: 0.3338
- Accuracy Metal: 0.3816
- Iou Mirror: 0.5226
- Accuracy Mirror: 0.5838
- Iou Paint Plaster Enamel: 0.7258
- Accuracy Paint Plaster Enamel: 0.7853
- Iou Paper: 0.6264
- Accuracy Paper: 0.6989
- Iou Pearl: 0.0
- Accuracy Pearl: 0.0
- Iou Photograph Painting: 0.2530
- Accuracy Photogr...
Source context: 8 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/mask2former-swin-large-apple-dms-run1
Accuracy Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
326.7486 1.7045 150 0.1341 0.1501 0.5649 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4609 0.5478 0.1275 0.1276 0.1313 0.1350 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7335 0.8068 0.0 0.0 0.0 0.0 0.7408 0.7989 0.0000 0.0000 0.0006 0.0006 0.0 0.0 0.4755 0.5489 0.7019 0.8123 0.0 0.0 0.0109 0.0110 0.0 0.0 0.0376 0.0383 0.0 0.0 0.6685 0.7607 0.5104 0.6265 0.0 0.0 0.0002 0.0002 0.0128 0.0131 0.0371 0.0381 0.0 0.0 0.0 0.0 0.6973 ...
