This model is a fine-tuned version of nvidia/mit-b4 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 0.0243
Mean Iou: 0.9582
Mean Accuracy: 0.9792
Overall Accuracy: 0.9965
Accuracy Unlabeled: 0.9981
Accuracy Numero: 0.9603
Iou Unlabeled: 0.9963
Iou Numero: 0.9200

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Numero	Iou Unlabeled	Iou Numero
0.1406	5.0	20	0.1672	0.7389	0.7497	0.9790	1.0000	0.4994	0.9785	0.4993
0.045	10.0	40	0.0498	0.9398	0.9476	0.9951	0.9994	0.8958	0.9949	0.8846
0.0361	15.0	60	0.0296	0.9575	0.9811	0.9964	0.9978	0.9643	0.9963	0.9187
0.026	20.0	80	0.0243	0.9582	0.9792	0.9965	0.9981	0.9603	0.9963	0.9200

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

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

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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