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Segformer B5 Finetuned Apple Dms Run10 | ST Studio
Ressources / Segformer B5 Finetuned Apple Dms Run10 Segformer B5 Finetuned Apple Dms Run10 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: 1.7623 Mean Iou: 0.4532 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: 1.7623 Mean Iou: 0.4532 Mean...
Type Other Modèle de base nvidia/segformer-b5-finetuned-ade-640-640 Version Revision 6670f97
Source du modèle
Type Other
Taille du fichier 323 MB
Modèle de base nvidia/segformer-b5-finetuned-ade-640-640
Version vRevision 6670f97
Mis à jour 27 sept. 2026
Téléchargements 10
Tâches Segmentation d'image
Source Hugging Face
segformer-b5-finetuned-apple-dms-run10
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:
Artefacts du modèle
1 artefact model.safetensors
safetensors · 323 MB · SHA-256 efe5b85c99b1…e32b · Hugging Face
Télécharger Extraits de sources
2 extraits
Loss: 1.7623
Mean Iou: 0.4532
Mean Accuracy: 0.5406
Overall Accuracy: 0.8224
Accuracy Animal Skin: 0.6755
Iou Animal Skin: 0.4518
Accuracy Bone Teeth Horn: 0.0
Iou Bone Teeth Horn: 0.0
Accuracy Brickwork: 0.7190
Iou Brickwork: 0.6032
Accuracy Cardboard: 0.6659
Iou Cardboard: 0.5084
Accuracy Carpet Rug: 0.8853
Iou Carpet Rug: 0.7646
Accuracy Ceiling Tile: 0.8605
Iou Ceiling Tile: 0.7578
Accuracy Ceramic: 0.7609
Iou Ceramic: 0.6274
Accuracy Chalkboard Blackboard: 0.7714
Iou Chalkboard Blackboard: 0.6618
Accuracy Clutter: 0.0209
Iou Clutter: 0.0189
Accuracy Concrete: 0.6176
Iou Concrete: 0.4249
Accuracy Cork Corkboard: 0.0777
Iou Cork Corkboard: 0.0771
Accuracy Engineered Stone: 0.0887
Iou Engineered Stone: 0.0831
Accuracy Fabric Cloth: 0.9012
Iou Fabric Cloth: 0.8021
Accuracy Fiberglass Wool: 0.0
Iou Fiberglass Wool: 0.0
Accuracy Fire: 0.3660
Iou Fire: 0.3238
Accuracy Foliage: 0.9401
Iou Foliage: 0.8457
Accuracy Food: 0.9134
Iou Food: 0.7870
Accuracy Fur: 0.9191
Iou Fur: 0.8267
Accuracy Gemstone Quartz: 0.0206
Iou Gemstone Quartz: 0.0165
Accuracy Glass: 0.7480
Iou Glass: 0.6279
Accuracy Hair: 0.8541
Iou Hair: 0.7449
Accuracy Ice: 0.2601
Iou Ice: 0.2271
Accuracy Leather: 0.6014
Iou Leather: 0.5052
Accuracy Liquid Non-water: 0.1274
Iou Liquid Non-water: 0.1127
Accuracy Metal: 0.4793
Iou Metal: 0.3624
Accuracy Mirror: 0.5963
Iou Mirror: 0.5059
Accuracy Paint Plaster Enamel: 0.8822
Iou Paint Plaster Enamel: 0.7548
Accuracy Paper: 0.7413
Iou Paper: 0.6046
Accuracy Pearl: 0.0
Iou Pearl: 0.0
Accuracy Photograph Painting: 0.4709
Iou Photograph Painting: 0.3654
Accuracy Plastic Clear: 0.3889
Iou Plastic Clear: 0.2853
Accuracy Plastic Non-clear: 0.5811
Iou Plastic Non-clear: 0.4103
Accuracy Rubber Latex: 0.2984
Iou Rubber Latex: 0.2706
Accuracy Sand: 0.6261
Iou Sand: 0.4917
Accuracy Skin Lips: 0.8534
Iou Skin Lips: 0.7521
Accuracy Sky: 0.9745
Iou Sky: 0.9388
Accuracy Snow: 0.7361
Iou Snow: 0.6369
Accuracy Soap: 0.0
Iou Soap: 0.0
Accuracy Soil Mud: 0.5831
Iou Soil Mud: 0.4231
Accuracy Sponge: 0.0
Iou Sponge: 0.0
Accuracy Stone Natural: 0.6938
Iou Stone Natural: 0.5581
Accuracy Stone Polished: 0.3082
Iou Stone Polished: 0.2441
Accuracy Styrofoam: 0.0
Iou Styrofoam: 0.0
Accuracy Tile: 0.8167
Iou Tile: 0.6844
Accuracy Wallpaper: 0.5244
Iou Wallpaper: 0.4070
Accuracy Water: 0.9011
Iou Water: 0.8107
Accuracy Wax: 0.4492
Iou Wax: 0.4449
Accuracy Whiteboard: 0.8354
Iou Whiteboard: 0.7040
Accuracy Wicker: 0.5684
Iou Wicker: 0.4837
Accuracy Wood: 0.8578
Iou Wood: 0.7432
Accuracy Wood Tree: 0.5182
Iou Wood Tree: 0.3877
Accuracy Asphalt: 0.6330
Iou Asphalt: 0.4985
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: 0.2
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 2.6193 1.7045 150 1.8642 0.2821 0.3476 0.7673 0.0 0.0 0.0 0.0 0.6057 0.4818 0.3378 0.2951 0.8666 0.6817 0.7559 0.7038 0.6468 0.5399 0.0 0.0 0.0 0.0 0.5856
---
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-run10
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-run10
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: 1.7623
- Mean Iou: 0.4532
- Mean Accuracy: 0.5406
- Overall Accuracy: 0.8224
- Accuracy Animal Skin: 0.6755
- Iou Animal Skin: 0.4518
- Accuracy Bone Teeth Horn: 0.0
- Iou Bone Teeth Horn: 0.0
- Accuracy Brickwork: 0.7190
- Iou Brickwork: 0.6032
- Accuracy Cardboard: 0.6659
- Iou Cardboard: 0.5084
- Accuracy Carpet Rug: 0.8853
- Iou Carpet Rug: 0.7646
- Accuracy Ceiling Tile: 0.8605
- Iou Ceiling Tile: 0.7578
- Accuracy Ceramic: 0.7609
- Iou Ceramic: 0.6274
- Accuracy Chalkboard Blackboard: 0.7714
- Iou Chalkboard Blackboard: 0.6618
- Accuracy Clutter: 0.0209
- Iou Clutter: 0.0189
- Accuracy Concrete: 0.6176
- Iou Concrete: 0.4249
- Accuracy Cork Corkboard: 0.0777
- Iou Cork Corkboard: 0.0771
- Accuracy Engineered Stone: 0.0887
- Iou Engineered Stone: 0.0831
- Accuracy Fabric Cloth: 0.9012
- Iou Fabric Cloth: 0.8021
- Accuracy Fiberglass Wool: 0.0
- Iou Fiberglass Wool: 0.0
- Accuracy Fire: 0.3660
- Iou Fire: 0.3238
- Accuracy Foliage: 0.9401
- Iou Foliage: 0.8457
- Accuracy Food: 0.9134
- Iou Food: 0.7870
- Accuracy Fur: 0.9191
- Iou Fur: 0.8267
- Accuracy Gemstone Quartz: 0.0206
- Iou Gemstone Quartz: 0.0165
- Accuracy Glass: 0.7480
- Iou Glass: 0.6279
- Accuracy Hair: 0.8541
- Iou Hair: 0.7449
- Accuracy Ice: 0.2601
- Iou Ice: 0.2271
- Accuracy Leather: 0.6014
- Iou Leather: 0.5052
- Accuracy Liquid Non-water: 0.1274
- Iou Liquid Non-water: 0.1127
- Accuracy Metal: 0.4793
- Iou Metal: 0.3624
- Accuracy Mirror: 0.5963
- Iou Mirror: 0.5059
- Accuracy Paint Plaster Enamel: 0.8822
- Iou Paint Plaster Enamel: 0.7548
- Accuracy Paper: 0.7413
- Iou Paper: 0.6046
- Accuracy Pearl: 0.0
- Iou Pearl: 0.0
- Accuracy...
Source context: 10 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo AllanK24/segformer-b5-finetuned-apple-dms-run10
Iou Chalkboard Blackboard
Accuracy Engineered Stone
Accuracy Liquid Non-water
Accuracy Paint Plaster Enamel
Accuracy Photograph Painting
Accuracy Plastic Non-clear
1.7355 3.4091 300 1.7668 0.4046 0.4972 0.8030 0.5913 0.4254 0.0 0.0 0.7720 0.5484 0.6033 0.4574 0.9162 0.6958 0.8729 0.7558 0.7240 0.5942 0.7488 0.6230 0.0 0.0 0.6174 0.3871 0.0 0.0 0.0 0.0 0.8968 0.7796 0.0 0.0 0.0 0.0 0.9396 0.8300 0.8924 0.7827 0.9300 0.8202 0.0 0.0 0.6984 0.5928 0.8213 0.7092 0.0002 0.0002 0.5808 0.4909 0.0 0.0 0.4542 0.3241 0.5424 0.4592 0.8673 0.7334 0.7827 0.5697 0.0 0.0 0.5307 0.3418 0.3438 0.2548 0.4151 0.3378 0.2082 0.1953 0.5337 0.4660 0.8315 0.7113 0.9733 0.9313 0.6864 0.6194 0.0 0.0 0.5607 0.3887 0.0 0.0 0.7327 0.4994...
