This model is a fine-tuned version of nvidia/segformer-b3-finetuned-cityscapes-1024-1024 on the Logiroad/route_background_semantic dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 4
eval_batch_size: 4
seed: 1337
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: polynomial
training_steps: 10000

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Découpe	Accuracy Autre réparation	Accuracy Glaçage ou ressuage	Accuracy Emergence	Iou Découpe	Iou Autre réparation	Iou Glaçage ou ressuage	Iou Emergence
0.2715	1.0	2427	0.2682	0.0521	0.0669	0.1828	nan	0.0813	0.2533	0.0	0.0	0.0766	0.2362	0.0	0.0
0.2815	2.0	4854	0.2682	0.1165	0.1436	0.1593	nan	0.1108	0.1982	0.0	0.4090	0.1014	0.1916	0.0	0.4057
0.2638	3.0	7281	0.2420	0.1664	0.2100	0.2564	nan	0.2346	0.3039	0.0030	0.5085	0.2128	0.2854	0.0030	0.4973
0.2703	4.0	9708	0.2333	0.1941	0.2475	0.3074	nan	0.2843	0.3612	0.0446	0.5473	0.2512	0.3383	0.0429	0.5320
0.2197	4.1203	10000	0.2360	0.1916	0.2447	0.2962	nan	0.2865	0.3437	0.0386	0.5549	0.2515	0.3230	0.0369	0.5379