segformer-b0-winter

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

• Loss: 0.1441
• Mean Iou: 0.8861
• Mean Accuracy: 0.9456
• Overall Accuracy: 0.9660
• Accuracy Unlabeled: nan
• Accuracy Object: nan
• Accuracy Road: 0.9769
• Accuracy Side walk: 0.8930
• Accuracy Car: 0.9347
• Accuracy Pedestrian: nan
• Accuracy Other: 0.9779
• Iou Unlabeled: nan
• Iou Object: nan
• Iou Road: 0.9197
• Iou Side walk: 0.8250
• Iou Car: 0.8319
• Iou Pedestrian: nan
• Iou Other: 0.9678

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Object	Accuracy Road	Accuracy Side walk	Accuracy Car	Accuracy Pedestrian	Accuracy Other	Iou Unlabeled	Iou Object	Iou Road	Iou Side walk	Iou Car	Iou Pedestrian	Iou Other
0.2207	4.0	20	0.2300	0.8576	0.9439	0.9583	nan	nan	0.9738	0.8767	0.9564	nan	0.9686	nan	nan	0.9018	0.8000	0.7670	nan	0.9617
0.1792	8.0	40	0.2126	0.8696	0.9457	0.9614	nan	nan	0.9768	0.8911	0.9444	nan	0.9706	nan	nan	0.9106	0.8122	0.7924	nan	0.9633
0.1527	12.0	60	0.1869	0.8769	0.9470	0.9634	nan	nan	0.9776	0.9023	0.9364	nan	0.9718	nan	nan	0.9180	0.8165	0.8085	nan	0.9647
0.1329	16.0	80	0.1787	0.8783	0.9429	0.9634	nan	nan	0.9772	0.8880	0.9314	nan	0.9749	nan	nan	0.9126	0.8117	0.8229	nan	0.9661
0.1746	20.0	100	0.1651	0.8864	0.9511	0.9668	nan	nan	0.9771	0.9126	0.9395	nan	0.9751	nan	nan	0.9258	0.8369	0.8157	nan	0.9671
0.1218	24.0	120	0.1652	0.8798	0.9444	0.9643	nan	nan	0.9791	0.8858	0.9370	nan	0.9757	nan	nan	0.9140	0.8156	0.8224	nan	0.9673
0.0816	28.0	140	0.1473	0.8921	0.9521	0.9684	nan	nan	0.9723	0.9199	0.9383	nan	0.9780	nan	nan	0.9299	0.8478	0.8231	nan	0.9676
0.0893	32.0	160	0.1490	0.8892	0.9502	0.9672	nan	nan	0.9749	0.9140	0.9354	nan	0.9766	nan	nan	0.9260	0.8384	0.825	nan	0.9673
0.0849	36.0	180	0.1517	0.8861	0.9476	0.9660	nan	nan	0.9791	0.8987	0.9367	nan	0.9760	nan	nan	0.9205	0.8258	0.8308	nan	0.9674
0.1625	40.0	200	0.1519	0.8843	0.9468	0.9654	nan	nan	0.9777	0.8938	0.9394	nan	0.9763	nan	nan	0.9176	0.8235	0.8289	nan	0.9673
0.1396	44.0	220	0.1500	0.8850	0.9476	0.9655	nan	nan	0.9791	0.8949	0.9408	nan	0.9757	nan	nan	0.9187	0.8223	0.8317	nan	0.9674
0.0931	48.0	240	0.1441	0.8861	0.9456	0.9660	nan	nan	0.9769	0.8930	0.9347	nan	0.9779	nan	nan	0.9197	0.8250	0.8319	nan	0.9678

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0