segformer-b0-finetuned-segments-sidewalk-oct-22 – AI Model by adsjnajefwnb | AlphaNeural AI

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segformer-b0-finetuned-segments-sidewalk-oct-22

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segformer-b0-finetuned-segments-sidewalk-oct-22

This model is a fine-tuned version of nvidia/mit-b0 on the adsjnajefwnb/di dataset. It achieves the following results on the evaluation set:

Loss: 1.1425
Mean Iou: 0.4608
Mean Accuracy: 0.6144
Overall Accuracy: 0.9524
Accuracy Unlabeled: nan
Accuracy Anqvan: nan
Accuracy Baohuxiang: nan
Accuracy Biaozhi: nan
Accuracy Dianlan: 0.9591
Accuracy Miehuoqi: 0.8841
Accuracy Zhaoming: 0.0
Iou Unlabeled: 0.0
Iou Anqvan: nan
Iou Baohuxiang: nan
Iou Biaozhi: nan
Iou Dianlan: 0.9591
Iou Miehuoqi: 0.8841
Iou Zhaoming: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Anqvan	Accuracy Baohuxiang	Accuracy Biaozhi	Accuracy Dianlan	Accuracy Miehuoqi	Accuracy Zhaoming	Iou Unlabeled	Iou Anqvan	Iou Baohuxiang	Iou Biaozhi	Iou Dianlan	Iou Miehuoqi	Iou Zhaoming
1.5348	10.0	20	1.8080	0.3181	0.6370	0.9720	nan	nan	nan	nan	0.9776	0.9334	0.0	0.0	nan	0.0	0.0	0.9759	0.9328	0.0
1.5003	20.0	40	1.5287	0.3865	0.6442	0.9726	nan	nan	nan	nan	0.9773	0.9552	0.0	0.0	nan	nan	0.0	0.9773	0.9552	0.0
1.4909	30.0	60	1.2543	0.4770	0.6359	0.9695	nan	nan	nan	nan	0.9750	0.9328	0.0	0.0	nan	nan	nan	0.9750	0.9328	0.0
1.331	40.0	80	1.1463	0.4671	0.6229	0.9613	nan	nan	nan	nan	0.9677	0.9009	0.0	0.0	nan	nan	nan	0.9677	0.9009	0.0
1.0843	50.0	100	1.1425	0.4608	0.6144	0.9524	nan	nan	nan	nan	0.9591	0.8841	0.0	0.0	nan	nan	nan	0.9591	0.8841	0.0

Framework versions

Transformers 4.46.3
Pytorch 1.12.1+cu113
Datasets 3.1.0
Tokenizers 0.20.3