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grayphd/
segformer-b0-finetuned-segments-sidewalk-2

Name: segformer-b0-finetuned-segments-sidewalk-2
Availability: InStock

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segformer-b0-finetuned-segments-sidewalk-2

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

Loss: 1.8299
Mean Iou: 0.1367
Mean Accuracy: 0.1860
Overall Accuracy: 0.6943
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.7986
Accuracy Flat-sidewalk: 0.8984
Accuracy Flat-crosswalk: 0.0
Accuracy Flat-cyclinglane: 0.0233
Accuracy Flat-parkingdriveway: 0.0008
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.0
Accuracy Human-person: 0.0
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.8604
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: nan
Accuracy Vehicle-tramtrain: 0.0
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.0
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.8665
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.0
Accuracy Construction-fenceguardrail: 0.0
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: 0.0
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.0
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.9388
Accuracy Nature-terrain: 0.7081
Accuracy Sky: 0.8565
Accuracy Void-ground: 0.0001
Accuracy Void-dynamic: 0.0
Accuracy Void-static: 0.0
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.4128
Iou Flat-sidewalk: 0.7214
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.0233
Iou Flat-parkingdriveway: 0.0008
Iou Flat-railtrack: nan
Iou Flat-curb: 0.0
Iou Human-person: 0.0
Iou Human-rider: 0.0
Iou Vehicle-car: 0.6003
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: nan
Iou Vehicle-tramtrain: 0.0
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.0
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.5461
Iou Construction-door: 0.0
Iou Construction-wall: 0.0
Iou Construction-fenceguardrail: 0.0
Iou Construction-bridge: 0.0
Iou Construction-tunnel: 0.0
Iou Construction-stairs: 0.0
Iou Object-pole: 0.0
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.7232
Iou Nature-terrain: 0.5549
Iou Sky: 0.7907
Iou Void-ground: 0.0001
Iou Void-dynamic: 0.0
Iou Void-static: 0.0
Iou Void-unclear: 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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Vehicle-car	Accuracy Vehicle-bus	Accuracy Construction-building	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-ground	Accuracy Void-dynamic	Accuracy Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bus	Iou Construction-building	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-ground	Iou Void-dynamic	Iou Void-static
2.6535	1.5385	20	2.8741	0.0846	0.1365	0.6009	nan	0.2219	0.9273	0.0017	0.0042	nan	0.0000	0.0014	0.8438	nan	0.7341	0.0015	0.0019	0.9709	0.1359	0.5242	0.0	0.0000	0.0000	0.0	0.1852	0.5896	0.0017	0.0041	0.0	0.0000	0.0013	0.4925	0.0	0.4757	0.0015	0.0018	0.5807	0.1127	0.5151	0.0	0.0000	0.0000
2.2209	3.0769	40	2.1895	0.1089	0.1592	0.6529	nan	0.6642	0.9009	0.0020	0.0002	nan	0.0	0.0	0.8372	nan	0.8318	0.0001	0.0	0.9586	0.2673	0.6333	0.0000	0.0	0.0	nan	0.3818	0.6832	0.0020	0.0002	0.0	0.0	0.0	0.5601	nan	0.4918	0.0001	0.0	0.6325	0.2290	0.6137	0.0000	0.0	0.0
1.9209	4.6154	60	1.9240	0.1295	0.1779	0.6803	nan	0.7491	0.8970	0.0037	0.0003	nan	0.0	0.0001	0.8615	nan	0.8579	0.0000	0.0	0.9436	0.5907	0.7886	0.0003	0.0	0.0	nan	0.3990	0.7030	0.0037	0.0003	nan	0.0	0.0001	0.5788	nan	0.5252	0.0000	0.0	0.7042	0.4845	0.7438	0.0003	0.0	0.0
1.9014	6.1538	80	1.8370	0.1346	0.1829	0.6899	nan	0.7851	0.8979	0.0146	0.0006	nan	0.0	0.0000	0.8365	nan	0.8694	0.0	0.0	0.9449	0.6680	0.8361	0.0001	0.0	0.0	nan	0.4097	0.7159	0.0145	0.0006	nan	0.0	0.0000	0.6037	nan	0.5376	0.0	0.0	0.7130	0.5332	0.7782	0.0001	0.0	0.0
1.8127	7.6923	100	1.8299	0.1367	0.1860	0.6943	nan	0.7986	0.8984	0.0233	0.0008	nan	0.0	0.0	0.8604	nan	0.8665	0.0	0.0	0.9388	0.7081	0.8565	0.0001	0.0	0.0	nan	0.4128	0.7214	0.0233	0.0008	nan	0.0	0.0	0.6003	nan	0.5461	0.0	0.0	0.7232	0.5549	0.7907	0.0001	0.0	0.0

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

grayphd/segformer-b0-finetuned-segments-sidewalk-2

segformer-b0-finetuned-segments-sidewalk-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

grayphd/
segformer-b0-finetuned-segments-sidewalk-2