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Segformer B5 Finetuned Apple Dms Run9 | ST Studio
Recursos / Segformer B5 Finetuned Apple Dms Run9 Segformer B5 Finetuned Apple Dms Run9 This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set: Loss: 1.2879 Mean Iou: 0.4724 Mean...
This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the AllanK24/apple-dms-materials dataset. It achieves the following results on the evaluation set: Loss: 1.2879 Mean Iou: 0.4724 Mean...
Tipo Other Modelo base nvidia/segformer-b5-finetuned-ade-640-640 Versão Revision a2d9137
Fonte do modelo
Tipo Other
Tamanho do arquivo 323 MB
Modelo base nvidia/segformer-b5-finetuned-ade-640-640
Versão vRevision a2d9137
Atualizado 7 de fev. de 2026
Downloads 6
Tarefas Segmentação de imagem
Fonte Hugging Face
segformer-b5-finetuned-apple-dms-run9
This model is a fine-tuned version of nvidia/segformer-b5-finetuned-ade-640-640 on the AllanK24/apple-dms-materials dataset.
It achieves the following results on the evaluation set:
Artefatos de modelo
1 artefato model.safetensors
safetensors · 323 MB · SHA-256 ae884fda1732…25ff · Hugging Face
Baixar Trechos de fonte
2 trechos
Loss: 1.2879
Mean Iou: 0.4724
Mean Accuracy: 0.5648
Overall Accuracy: 0.8240
Accuracy Animal Skin: 0.6669
Iou Animal Skin: 0.4954
Accuracy Bone Teeth Horn: 0.0000
Iou Bone Teeth Horn: 0.0000
Accuracy Brickwork: 0.7143
Iou Brickwork: 0.5850
Accuracy Cardboard: 0.6449
Iou Cardboard: 0.4995
Accuracy Carpet Rug: 0.8798
Iou Carpet Rug: 0.7598
Accuracy Ceiling Tile: 0.8870
Iou Ceiling Tile: 0.7860
Accuracy Ceramic: 0.7790
Iou Ceramic: 0.6400
Accuracy Chalkboard Blackboard: 0.7807
Iou Chalkboard Blackboard: 0.6577
Accuracy Clutter: 0.0578
Iou Clutter: 0.0491
Accuracy Concrete: 0.6090
Iou Concrete: 0.4245
Accuracy Cork Corkboard: 0.0904
Iou Cork Corkboard: 0.0887
Accuracy Engineered Stone: 0.1158
Iou Engineered Stone: 0.1017
Accuracy Fabric Cloth: 0.9022
Iou Fabric Cloth: 0.8034
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.5132
Iou Fire: 0.4591
Accuracy Foliage: 0.9381
Iou Foliage: 0.8476
Accuracy Food: 0.9138
Iou Food: 0.7933
Accuracy Fur: 0.9282
Iou Fur: 0.8474
Accuracy Gemstone Quartz: 0.5965
Iou Gemstone Quartz: 0.4286
Accuracy Glass: 0.7541
Iou Glass: 0.6325
Accuracy Hair: 0.8567
Iou Hair: 0.7472
Accuracy Ice: 0.2807
Iou Ice: 0.2218
Accuracy Leather: 0.6177
Iou Leather: 0.5098
Accuracy Liquid Non-water: 0.3093
Iou Liquid Non-water: 0.2761
Accuracy Metal: 0.4917
Iou Metal: 0.3705
Accuracy Mirror: 0.5999
Iou Mirror: 0.5131
Accuracy Paint Plaster Enamel: 0.8792
Iou Paint Plaster Enamel: 0.7555
Accuracy Paper: 0.7460
Iou Paper: 0.6039
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4887
Iou Photograph Painting: 0.3704
Accuracy Plastic Clear: 0.3719
Iou Plastic Clear: 0.2842
Accuracy Plastic Non-clear: 0.5611
Iou Plastic Non-clear: 0.4098
Accuracy Rubber Latex: 0.3284
Iou Rubber Latex: 0.2964
Accuracy Sand: 0.6453
Iou Sand: 0.5073
Accuracy Skin Lips: 0.8633
Iou Skin Lips: 0.7585
Accuracy Sky: 0.9738
Iou Sky: 0.9361
Accuracy Snow: 0.6948
Iou Snow: 0.5942
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5642
Iou Soil Mud: 0.4117
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.7217
Iou Stone Natural: 0.5622
Accuracy Stone Polished: 0.3487
Iou Stone Polished: 0.2630
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8132
Iou Tile: 0.6839
Accuracy Wallpaper: 0.6192
Iou Wallpaper: 0.4637
Accuracy Water: 0.9112
Iou Water: 0.8091
Accuracy Wax: 0.5252
Iou Wax: 0.4924
Accuracy Whiteboard: 0.7797
Iou Whiteboard: 0.6631
Accuracy Wicker: 0.5724
Iou Wicker: 0.5007
Accuracy Wood: 0.8634
Iou Wood: 0.7459
Accuracy Wood Tree: 0.5530
Iou Wood Tree: 0.4346
Accuracy Asphalt: 0.6158
Iou Asphalt: 0.4780
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
label_smoothing_factor: 0.1
Training results Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Animal Skin Iou Animal Skin Accuracy Bone Teeth Horn Iou Bone Teeth Horn Accuracy Brickwork Iou Brickwork Accuracy Cardboard Iou Cardboard Accuracy Carpet Rug Iou Carpet Rug Accuracy Ceiling Tile Iou Ceiling Tile Accuracy Ceramic Iou Ceramic Accuracy Chalkboard Blackboard 2.3390 1.7045 150 1.3873 0.2906 0.3589 0.7696 0.0 0.0 0.0 0.0 0.5911 0.4554 0.2521 0.2423 0.8704 0.6817 0.8120 0.7332 0.6779 0.5571 0.0244 0.0244 0.0 0.0 0.5575
---
library_name: transformers
license: other
base_model: nvidia/segformer-b5-finetuned-ade-640-640
tags:
- vision
- image-segmentation
- segformer
- material-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-apple-dms-run9
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. -->
# segformer-b5-finetuned-apple-dms-run9
This model is a fine-tuned version of [nvidia/segformer-b5-finetuned-ade-640-640](https://huggingface.co/nvidia/segformer-b5-finetuned-ade-640-640) on the AllanK24/apple-dms-materials dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2879
- Mean Iou: 0.4724
- Mean Accuracy: 0.5648
- Overall Accuracy: 0.8240
- Accuracy Animal Skin: 0.6669
- Iou Animal Skin: 0.4954
- Accuracy Bone Teeth Horn: 0.0000
- Iou Bone Teeth Horn: 0.0000
- Accuracy Brickwork: 0.7143
- Iou Brickwork: 0.5850
- Accuracy Cardboard: 0.6449
- Iou Cardboard: 0.4995
- Accuracy Carpet Rug: 0.8798
- Iou Carpet Rug: 0.7598
- Accuracy Ceiling Tile: 0.8870
- Iou Ceiling Tile: 0.7860
- Accuracy Ceramic: 0.7790
- Iou Ceramic: 0.6400
- Accuracy Chalkboard Blackboard: 0.7807
- Iou Chalkboard Blackboard: 0.6577
- Accuracy Clutter: 0.0578
- Iou Clutter: 0.0491
- Accuracy Concrete: 0.6090
- Iou Concrete: 0.4245
- Accuracy Cork Corkboard: 0.0904
- Iou Cork Corkboard: 0.0887
- Accuracy Engineered Stone: 0.1158
- Iou Engineered Stone: 0.1017
- Accuracy Fabric Cloth: 0.9022
- Iou Fabric Cloth: 0.8034
- Accuracy Fiberglass Wool: 0.0
- Iou Fiberglass Wool: 0.0
- Accuracy Fire: 0.5132
- Iou Fire: 0.4591
- Accuracy Foliage: 0.9381
- Iou Foliage: 0.8476
- Accuracy Food: 0.9138
- Iou Food: 0.7933
- Accuracy Fur: 0.9282
- Iou Fur: 0.8474
- Accuracy Gemstone Quartz: 0.5965
- Iou Gemstone Quartz: 0.4286
- Accuracy Glass: 0.7541
- Iou Glass: 0.6325
- Accuracy Hair: 0.8567
- Iou Hair: 0.7472
- Accuracy Ice: 0.2807
- Iou Ice: 0.2218
- Accuracy Leather: 0.6177
- Iou Leather: 0.5098
- Accuracy Liquid Non-water: 0.3093
- Iou Liquid Non-water: 0.2761
- Accuracy Metal: 0.4917
- Iou Metal: 0.3705
- Accuracy Mirror: 0.5999
- Iou Mirror: 0.5131
- Accuracy Paint Plaster Enamel: 0.8792
- Iou Paint Plaster Enamel: 0.7555
- Accuracy Paper: 0.7460
- Iou Paper: 0.6039
- Accuracy Pearl: 0.0
- Iou Pearl: 0.0
- Accu...
Source context: 6 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/segformer-b5-finetuned-apple-dms-run9
Iou Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
1.2336 3.4091 300 1.2783 0.4077 0.5000 0.8047 0.4999 0.3737 0.0 0.0 0.7482 0.5652 0.5903 0.4623 0.9128 0.6888 0.8757 0.7657 0.7210 0.6021 0.7550 0.5898 0.0 0.0 0.6337 0.3852 0.0 0.0 0.0 0.0 0.9002 0.7811 0.0 0.0 0.0001 0.0001 0.9394 0.8322 0.9096 0.7764 0.9300 0.8306 0.0 0.0 0.6882 0.5918 0.8126 0.7093 0.0027 0.0027 0.5516 0.4738 0.0 0.0 0.4638 0.3340 0.5795 0.4795 0.8712 0.7364 0.7824 0.5690 0.0 0.0 0.5541 0.3514 0.3345 0.2470 0.4359 0.3490 0.2426 0.2211 0.7089 0.5633 0.8241 0.7167 0.9711 0.9269 0.6966 0.6169 0.0 0.0 0.5353 0.4179 0.0 0.0 0.7151 ...
