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

Loss: 2.2162
Mean Iou: 0.0387
Mean Accuracy: 0.1129
Overall Accuracy: 0.1050
Accuracy Backgound : nan
Accuracy Tdz: 0.0493
Accuracy Aim: 0.2144
Accuracy Desig: 0.0922
Accuracy Rwythr: 0.1765
Accuracy Thrbar: 0.0140
Accuracy Disp: 0.2710
Accuracy Chevron: 0.0023
Accuracy Arrow: 0.0834
Iou Backgound : 0.0
Iou Tdz: 0.0399
Iou Aim: 0.1158
Iou Desig: 0.0443
Iou Rwythr: 0.0980
Iou Thrbar: 0.0131
Iou Disp: 0.0266
Iou Chevron: 0.0020
Iou Arrow: 0.0085

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: 8
eval_batch_size: 8
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
training_steps: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Backgound	Accuracy Tdz	Accuracy Aim	Accuracy Desig	Accuracy Rwythr	Accuracy Thrbar	Accuracy Disp	Accuracy Chevron	Accuracy Arrow	Iou Backgound	Iou Tdz	Iou Aim	Iou Desig	Iou Rwythr	Iou Thrbar	Iou Disp	Iou Chevron	Iou Arrow
2.2475	0.0455	2	2.2162	0.0387	0.1129	0.1050	nan	0.0493	0.2144	0.0922	0.1765	0.0140	0.2710	0.0023	0.0834	0.0	0.0399	0.1158	0.0443	0.0980	0.0131	0.0266	0.0020	0.0085

Framework versions

Transformers 4.43.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Spatiallysaying/segformer-finetuned-rwymarkings-2-steps

segformer-finetuned-rwymarkings-2-steps

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Spatiallysaying/
segformer-finetuned-rwymarkings-2-steps