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Segformer B5 Finetuned Apple Dms Run7 | ST Studio
Recursos / Segformer B5 Finetuned Apple Dms Run7 Segformer B5 Finetuned Apple Dms Run7 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: 1.3192 Mean Iou: 0.3630 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: 1.3192 Mean Iou: 0.3630 Mean...
Tipo Other Modelo base nvidia/segformer-b5-finetuned-ade-640-640 Versão Revision 62c2286
Fonte do modelo
Tipo Other
Tamanho do arquivo 323 MB
Modelo base nvidia/segformer-b5-finetuned-ade-640-640
Versão vRevision 62c2286
Atualizado 27 de set. de 2026
Downloads 10
Tarefas Segmentação de imagem
Fonte Hugging Face
segformer-b5-finetuned-apple-dms-run7
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 a3946e3688c6…c1ae · Hugging Face
Baixar Trechos de fonte
2 trechos
Loss: 1.3192
Mean Iou: 0.3630
Mean Accuracy: 0.4423
Overall Accuracy: 0.7928
Accuracy Animal Skin: 0.0003
Iou Animal Skin: 0.0003
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.6738
Iou Brickwork: 0.5345
Accuracy Cardboard: 0.5569
Iou Cardboard: 0.4395
Accuracy Carpet Rug: 0.8605
Iou Carpet Rug: 0.7131
Accuracy Ceiling Tile: 0.8633
Iou Ceiling Tile: 0.7422
Accuracy Ceramic: 0.7179
Iou Ceramic: 0.5672
Accuracy Chalkboard Blackboard: 0.4211
Iou Chalkboard Blackboard: 0.3839
Accuracy Clutter: 0.0
Iou Clutter: 0.0
Accuracy Concrete: 0.5419
Iou Concrete: 0.3820
Accuracy Cork Corkboard: 0.0
Iou Cork Corkboard: 0.0
Accuracy Engineered Stone: 0.0
Iou Engineered Stone: 0.0
Accuracy Fabric Cloth: 0.8896
Iou Fabric Cloth: 0.7605
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.0
Iou Fire: 0.0
Accuracy Foliage: 0.9353
Iou Foliage: 0.8202
Accuracy Food: 0.8894
Iou Food: 0.7479
Accuracy Fur: 0.9129
Iou Fur: 0.7322
Accuracy Gemstone Quartz: 0.0
Iou Gemstone Quartz: 0.0
Accuracy Glass: 0.7364
Iou Glass: 0.5750
Accuracy Hair: 0.8049
Iou Hair: 0.6815
Accuracy Ice: 0.0
Iou Ice: 0.0
Accuracy Leather: 0.4800
Iou Leather: 0.3915
Accuracy Liquid Non-water: 0.0
Iou Liquid Non-water: 0.0
Accuracy Metal: 0.3939
Iou Metal: 0.3004
Accuracy Mirror: 0.5509
Iou Mirror: 0.4350
Accuracy Paint Plaster Enamel: 0.8588
Iou Paint Plaster Enamel: 0.7243
Accuracy Paper: 0.7307
Iou Paper: 0.5634
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4050
Iou Photograph Painting: 0.3124
Accuracy Plastic Clear: 0.1821
Iou Plastic Clear: 0.1597
Accuracy Plastic Non-clear: 0.4768
Iou Plastic Non-clear: 0.3419
Accuracy Rubber Latex: 0.0
Iou Rubber Latex: 0.0
Accuracy Sand: 0.4155
Iou Sand: 0.3879
Accuracy Skin Lips: 0.8052
Iou Skin Lips: 0.6839
Accuracy Sky: 0.9688
Iou Sky: 0.9233
Accuracy Snow: 0.6109
Iou Snow: 0.5618
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5931
Iou Soil Mud: 0.3909
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6618
Iou Stone Natural: 0.4872
Accuracy Stone Polished: 0.1331
Iou Stone Polished: 0.1252
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.7938
Iou Tile: 0.6378
Accuracy Wallpaper: 0.5247
Iou Wallpaper: 0.4280
Accuracy Water: 0.9073
Iou Water: 0.7776
Accuracy Wax: 0.0
Iou Wax: 0.0
Accuracy Whiteboard: 0.7118
Iou Whiteboard: 0.5000
Accuracy Wicker: 0.3448
Iou Wicker: 0.3208
Accuracy Wood: 0.8547
Iou Wood: 0.7110
Accuracy Wood Tree: 0.2178
Iou Wood Tree: 0.1922
Accuracy Asphalt: 0.5736
Iou Asphalt: 0.4419
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
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 3.5942 1.7045 150 2.5848 0.0639 0.0960 0.5317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0984 0.0952 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
---
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-run7
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-run7
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: 1.3192
- Mean Iou: 0.3630
- Mean Accuracy: 0.4423
- Overall Accuracy: 0.7928
- Accuracy Animal Skin: 0.0003
- Iou Animal Skin: 0.0003
- Accuracy Bone Teeth Horn: 0.0
- Iou Bone Teeth Horn: 0.0
- Accuracy Brickwork: 0.6738
- Iou Brickwork: 0.5345
- Accuracy Cardboard: 0.5569
- Iou Cardboard: 0.4395
- Accuracy Carpet Rug: 0.8605
- Iou Carpet Rug: 0.7131
- Accuracy Ceiling Tile: 0.8633
- Iou Ceiling Tile: 0.7422
- Accuracy Ceramic: 0.7179
- Iou Ceramic: 0.5672
- Accuracy Chalkboard Blackboard: 0.4211
- Iou Chalkboard Blackboard: 0.3839
- Accuracy Clutter: 0.0
- Iou Clutter: 0.0
- Accuracy Concrete: 0.5419
- Iou Concrete: 0.3820
- Accuracy Cork Corkboard: 0.0
- Iou Cork Corkboard: 0.0
- Accuracy Engineered Stone: 0.0
- Iou Engineered Stone: 0.0
- Accuracy Fabric Cloth: 0.8896
- Iou Fabric Cloth: 0.7605
- Accuracy Fiberglass Wool: 0.0
- Iou Fiberglass Wool: 0.0
- Accuracy Fire: 0.0
- Iou Fire: 0.0
- Accuracy Foliage: 0.9353
- Iou Foliage: 0.8202
- Accuracy Food: 0.8894
- Iou Food: 0.7479
- Accuracy Fur: 0.9129
- Iou Fur: 0.7322
- Accuracy Gemstone Quartz: 0.0
- Iou Gemstone Quartz: 0.0
- Accuracy Glass: 0.7364
- Iou Glass: 0.5750
- Accuracy Hair: 0.8049
- Iou Hair: 0.6815
- Accuracy Ice: 0.0
- Iou Ice: 0.0
- Accuracy Leather: 0.4800
- Iou Leather: 0.3915
- Accuracy Liquid Non-water: 0.0
- Iou Liquid Non-water: 0.0
- Accuracy Metal: 0.3939
- Iou Metal: 0.3004
- Accuracy Mirror: 0.5509
- Iou Mirror: 0.4350
- Accuracy Paint Plaster Enamel: 0.8588
- Iou Paint Plaster Enamel: 0.7243
- Accuracy Paper: 0.7307
- Iou Paper: 0.5634
- Accuracy Pearl: 0.0
- Iou Pearl: 0.0
- Accuracy Photograph Painting: 0.4050
- Iou Photograp...
Source context: 10 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/segformer-b5-finetuned-apple-dms-run7
Iou Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
1.9460 3.4091 300 1.6887 0.1583 0.2074 0.6906 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7844 0.5407 0.7715 0.6552 0.0 0.0 0.0 0.0 0.0 0.0 0.0359 0.0351 0.0 0.0 0.0 0.0 0.8989 0.6418 0.0 0.0 0.0 0.0 0.9376 0.6128 0.6183 0.5662 0.0000 0.0000 0.0 0.0 0.6617 0.4543 0.4619 0.4115 0.0 0.0 0.0 0.0 0.0 0.0 0.0840 0.0743 0.0000 0.0000 0.8458 0.6539 0.6632 0.4467 0.0 0.0 0.0 0.0 0.0 0.0 0.2981 0.2058 0.0 0.0 0.0 0.0 0.2557 0.2316 0.9591 0.9030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0602 0.0593 0.0 0.0...
