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Segformer B5 Finetuned Apple Dms Run11 | ST Studio
Recursos / Segformer B5 Finetuned Apple Dms Run11 Segformer B5 Finetuned Apple Dms Run11 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: 1.2034 Mean Iou: 0.4595 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: 1.2034 Mean Iou: 0.4595 Mean...
Tipo Other Modelo base nvidia/segformer-b5-finetuned-ade-640-640 Versión Revision ea9bff8
Fuente del modelo
Tipo Other
Tamaño del archivo 323 MB
Modelo base nvidia/segformer-b5-finetuned-ade-640-640
Versión vRevision ea9bff8
Actualizado 27 sept 2026
Descargas 16
Tareas Segmentación de imágenes
Fuente Hugging Face
segformer-b5-finetuned-apple-dms-run11
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:
Artefactos del modelo
1 artefacto model.safetensors
safetensors · 323 MB · SHA-256 d2e1da2e525c…fc72 · Hugging Face
Descargar Extractos de fuentes
2 extractos
Loss: 1.2034
Mean Iou: 0.4595
Mean Accuracy: 0.5491
Overall Accuracy: 0.8239
Accuracy Animal Skin: 0.5919
Iou Animal Skin: 0.4132
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.7186
Iou Brickwork: 0.5878
Accuracy Cardboard: 0.7042
Iou Cardboard: 0.5463
Accuracy Carpet Rug: 0.8807
Iou Carpet Rug: 0.7729
Accuracy Ceiling Tile: 0.8715
Iou Ceiling Tile: 0.7782
Accuracy Ceramic: 0.7940
Iou Ceramic: 0.6572
Accuracy Chalkboard Blackboard: 0.7596
Iou Chalkboard Blackboard: 0.6748
Accuracy Clutter: 0.0002
Iou Clutter: 0.0002
Accuracy Concrete: 0.5929
Iou Concrete: 0.4385
Accuracy Cork Corkboard: 0.0
Iou Cork Corkboard: 0.0
Accuracy Engineered Stone: 0.0222
Iou Engineered Stone: 0.0222
Accuracy Fabric Cloth: 0.9068
Iou Fabric Cloth: 0.7972
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.4037
Iou Fire: 0.3584
Accuracy Foliage: 0.9342
Iou Foliage: 0.8377
Accuracy Food: 0.8947
Iou Food: 0.7873
Accuracy Fur: 0.9217
Iou Fur: 0.8129
Accuracy Gemstone Quartz: 0.0520
Iou Gemstone Quartz: 0.0381
Accuracy Glass: 0.7609
Iou Glass: 0.6317
Accuracy Hair: 0.8536
Iou Hair: 0.7276
Accuracy Ice: 0.4306
Iou Ice: 0.3222
Accuracy Leather: 0.6377
Iou Leather: 0.5276
Accuracy Liquid Non-water: 0.0682
Iou Liquid Non-water: 0.0655
Accuracy Metal: 0.5094
Iou Metal: 0.3766
Accuracy Mirror: 0.6544
Iou Mirror: 0.5306
Accuracy Paint Plaster Enamel: 0.8744
Iou Paint Plaster Enamel: 0.7547
Accuracy Paper: 0.7554
Iou Paper: 0.6071
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4862
Iou Photograph Painting: 0.3616
Accuracy Plastic Clear: 0.3503
Iou Plastic Clear: 0.2723
Accuracy Plastic Non-clear: 0.5506
Iou Plastic Non-clear: 0.4171
Accuracy Rubber Latex: 0.3110
Iou Rubber Latex: 0.2692
Accuracy Sand: 0.7507
Iou Sand: 0.5800
Accuracy Skin Lips: 0.8420
Iou Skin Lips: 0.7321
Accuracy Sky: 0.9571
Iou Sky: 0.9293
Accuracy Snow: 0.7200
Iou Snow: 0.6666
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5783
Iou Soil Mud: 0.4410
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6875
Iou Stone Natural: 0.5539
Accuracy Stone Polished: 0.3631
Iou Stone Polished: 0.3075
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8126
Iou Tile: 0.6766
Accuracy Wallpaper: 0.6609
Iou Wallpaper: 0.5012
Accuracy Water: 0.9142
Iou Water: 0.8189
Accuracy Wax: 0.5010
Iou Wax: 0.4732
Accuracy Whiteboard: 0.8216
Iou Whiteboard: 0.6930
Accuracy Wicker: 0.6048
Iou Wicker: 0.5193
Accuracy Wood: 0.8714
Iou Wood: 0.7396
Accuracy Wood Tree: 0.5341
Iou Wood Tree: 0.3701
Accuracy Asphalt: 0.6402
Iou Asphalt: 0.5071
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 2.4072 1.7045 150 1.4449 0.2578 0.3205 0.7517 0.0 0.0 0.0 0.0 0.4948 0.3764 0.2556 0.2399 0.7925 0.6477 0.8360 0.7015 0.6684 0.5413 0.0 0.0 0.0 0.0 0.4411
---
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-run11
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-run11
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: 1.2034
- Mean Iou: 0.4595
- Mean Accuracy: 0.5491
- Overall Accuracy: 0.8239
- Accuracy Animal Skin: 0.5919
- Iou Animal Skin: 0.4132
- Accuracy Bone Teeth Horn: 0.0
- Iou Bone Teeth Horn: 0.0
- Accuracy Brickwork: 0.7186
- Iou Brickwork: 0.5878
- Accuracy Cardboard: 0.7042
- Iou Cardboard: 0.5463
- Accuracy Carpet Rug: 0.8807
- Iou Carpet Rug: 0.7729
- Accuracy Ceiling Tile: 0.8715
- Iou Ceiling Tile: 0.7782
- Accuracy Ceramic: 0.7940
- Iou Ceramic: 0.6572
- Accuracy Chalkboard Blackboard: 0.7596
- Iou Chalkboard Blackboard: 0.6748
- Accuracy Clutter: 0.0002
- Iou Clutter: 0.0002
- Accuracy Concrete: 0.5929
- Iou Concrete: 0.4385
- Accuracy Cork Corkboard: 0.0
- Iou Cork Corkboard: 0.0
- Accuracy Engineered Stone: 0.0222
- Iou Engineered Stone: 0.0222
- Accuracy Fabric Cloth: 0.9068
- Iou Fabric Cloth: 0.7972
- Accuracy Fiberglass Wool: 0.0
- Iou Fiberglass Wool: 0.0
- Accuracy Fire: 0.4037
- Iou Fire: 0.3584
- Accuracy Foliage: 0.9342
- Iou Foliage: 0.8377
- Accuracy Food: 0.8947
- Iou Food: 0.7873
- Accuracy Fur: 0.9217
- Iou Fur: 0.8129
- Accuracy Gemstone Quartz: 0.0520
- Iou Gemstone Quartz: 0.0381
- Accuracy Glass: 0.7609
- Iou Glass: 0.6317
- Accuracy Hair: 0.8536
- Iou Hair: 0.7276
- Accuracy Ice: 0.4306
- Iou Ice: 0.3222
- Accuracy Leather: 0.6377
- Iou Leather: 0.5276
- Accuracy Liquid Non-water: 0.0682
- Iou Liquid Non-water: 0.0655
- Accuracy Metal: 0.5094
- Iou Metal: 0.3766
- Accuracy Mirror: 0.6544
- Iou Mirror: 0.5306
- Accuracy Paint Plaster Enamel: 0.8744
- Iou Paint Plaster Enamel: 0.7547
- Accuracy Paper: 0.7554
- Iou Paper: 0.6071
- Accuracy Pearl: 0.0
- Iou Pearl: 0.0
- Accuracy Photo...
Source context: 16 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/segformer-b5-finetuned-apple-dms-run11
Iou Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
1.3990 3.4091 300 1.3133 0.3801 0.4630 0.7905 0.4450 0.3464 0.0 0.0 0.6816 0.5528 0.4601 0.4046 0.8308 0.7031 0.7919 0.7265 0.6966 0.5831 0.7040 0.5738 0.0 0.0 0.5429 0.3618 0.0 0.0 0.0 0.0 0.9026 0.7472 0.0 0.0 0.0 0.0 0.9325 0.8212 0.9245 0.7434 0.9159 0.7748 0.0 0.0 0.7021 0.5645 0.8353 0.6965 0.0 0.0 0.3668 0.3479 0.0 0.0 0.4559 0.3104 0.3370 0.3199 0.8592 0.7260 0.7652 0.5610 0.0 0.0 0.4873 0.3273 0.2128 0.1725 0.4911 0.3617 0.1564 0.1468 0.4271 0.3653 0.8131 0.6772 0.9616 0.9266 0.6957 0.6421 0.0 0.0 0.6067 0.4118 0.0 0.0 0.7065 0.4841...
