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: 0.0182
Mean Iou: 0.0441
Mean Accuracy: 0.0510
Overall Accuracy: 0.0800
Accuracy Backgound : nan
Accuracy Tdz: 0.0908
Accuracy Aim: 0.2203
Accuracy Desig: 0.0
Accuracy Rwythr: 0.0971
Accuracy Thrbar: 0.0
Accuracy Disp: 0.0
Accuracy Chevron: 0.0
Accuracy Arrow: 0.0
Iou Backgound : 0.0
Iou Tdz: 0.0818
Iou Aim: 0.2189
Iou Desig: 0.0
Iou Rwythr: 0.0958
Iou Thrbar: 0.0
Iou Disp: 0.0
Iou Chevron: 0.0
Iou Arrow: 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: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Backgound	Accuracy Tdz	Accuracy Aim	Accuracy Rwythr	Iou Tdz	Iou Aim	Iou Rwythr
1.6294	1.0	173	0.5448	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0
0.3371	2.0	346	0.1107	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0
0.0724	3.0	519	0.0483	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0
0.0508	4.0	692	0.0331	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0
0.0369	5.0	865	0.0289	0.0002	0.0002	0.0004	nan	0.0	0.0019	0.0	0.0	0.0019	0.0
0.0272	6.0	1038	0.0276	0.0106	0.0120	0.0195	nan	0.0107	0.0853	0.0	0.0105	0.0845	0.0
0.0258	7.0	1211	0.0233	0.0066	0.0075	0.0122	nan	0.0118	0.0480	0.0	0.0117	0.0480	0.0
0.0235	8.0	1384	0.0221	0.0150	0.0171	0.0277	nan	0.0233	0.1108	0.0024	0.0224	0.1107	0.0024
0.0213	9.0	1557	0.0209	0.0177	0.0200	0.0326	nan	0.0237	0.1351	0.0016	0.0231	0.1346	0.0016
0.0201	10.0	1730	0.0206	0.0277	0.0318	0.0512	nan	0.0595	0.1734	0.0211	0.0559	0.1726	0.0211
0.0203	11.0	1903	0.0198	0.0246	0.0281	0.0450	nan	0.0463	0.1512	0.0277	0.0432	0.1505	0.0277
0.0172	12.0	2076	0.0192	0.0377	0.0435	0.0690	nan	0.0744	0.2145	0.0592	0.0680	0.2119	0.0589
0.0168	13.0	2249	0.0189	0.0331	0.0381	0.0607	nan	0.0704	0.1884	0.0462	0.0645	0.1876	0.0461
0.0169	14.0	2422	0.0185	0.0383	0.0442	0.0701	nan	0.0786	0.2124	0.0628	0.0716	0.2112	0.0623
0.0172	15.0	2595	0.0184	0.0476	0.0551	0.0864	nan	0.0917	0.2463	0.1028	0.0830	0.2443	0.1013
0.0159	16.0	2768	0.0182	0.0523	0.0615	0.0964	nan	0.1202	0.2493	0.1225	0.1044	0.2468	0.1199
0.0163	17.0	2941	0.0181	0.0492	0.0571	0.0892	nan	0.0987	0.2414	0.1167	0.0885	0.2397	0.1146
0.0152	17.3410	3000	0.0182	0.0441	0.0510	0.0800	nan	0.0908	0.2203	0.0971	0.0818	0.2189	0.0958

Framework versions

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

Spatiallysaying/segformer-finetuned-rwymarkings-3k-steps

segformer-finetuned-rwymarkings-3k-steps

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Spatiallysaying/
segformer-finetuned-rwymarkings-3k-steps