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fshala/
segformer-finetuned-sidewalk-10k-steps

Name: segformer-finetuned-sidewalk-10k-steps
Availability: InStock

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segformer-finetuned-sidewalk-10k-steps

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: 2.3528
Mean Iou: 0.0705
Mean Accuracy: 0.1115
Overall Accuracy: 0.5632
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.5861
Accuracy Flat-sidewalk: 0.8653
Accuracy Flat-crosswalk: 0.0
Accuracy Flat-cyclinglane: 0.0136
Accuracy Flat-parkingdriveway: 0.0078
Accuracy Flat-railtrack: 0.0
Accuracy Flat-curb: 0.0
Accuracy Human-person: 0.0
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.0737
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: 0.0
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: 0.9444
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.0012
Accuracy Construction-fenceguardrail: 0.0000
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: nan
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.0
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.8841
Accuracy Nature-terrain: 0.2830
Accuracy Sky: 0.0216
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.0
Accuracy Void-static: 0.0
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.3993
Iou Flat-sidewalk: 0.6672
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.0135
Iou Flat-parkingdriveway: 0.0076
Iou Flat-railtrack: 0.0
Iou Flat-curb: 0.0
Iou Human-person: 0.0
Iou Human-rider: 0.0
Iou Vehicle-car: 0.0712
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
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: 0.2455
Iou Construction-door: 0.0
Iou Construction-wall: 0.0011
Iou Construction-fenceguardrail: 0.0000
Iou Construction-bridge: 0.0
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.0
Iou Object-pole: 0.0
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.6484
Iou Nature-terrain: 0.2515
Iou Sky: 0.0216
Iou Void-ground: 0.0
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: 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-crosswalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Human-rider	Accuracy Vehicle-car	Accuracy Vehicle-truck	Accuracy Vehicle-bus	Accuracy Vehicle-tramtrain	Accuracy Vehicle-motorcycle	Accuracy Vehicle-bicycle	Accuracy Vehicle-caravan	Accuracy Vehicle-cartrailer	Accuracy Construction-building	Accuracy Construction-door	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Construction-bridge	Accuracy Construction-tunnel	Accuracy Construction-stairs	Accuracy Object-pole	Accuracy Object-trafficsign	Accuracy Object-trafficlight	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-ground	Accuracy Void-dynamic	Accuracy Void-static	Accuracy Void-unclear	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-crosswalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Human-rider	Iou Vehicle-car	Iou Vehicle-truck	Iou Vehicle-bus	Iou Vehicle-tramtrain	Iou Vehicle-motorcycle	Iou Vehicle-bicycle	Iou Vehicle-caravan	Iou Vehicle-cartrailer	Iou Construction-building	Iou Construction-door	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-bridge	Iou Construction-tunnel	Iou Construction-stairs	Iou Object-pole	Iou Object-trafficsign	Iou Object-trafficlight	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-ground	Iou Void-dynamic	Iou Void-static	Iou Void-unclear
2.8114	0.93	100	2.3528	0.0705	0.1115	0.5632	nan	0.5861	0.8653	0.0	0.0136	0.0078	0.0	0.0	0.0	0.0	0.0737	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.9444	0.0	0.0012	0.0000	0.0	nan	0.0	0.0	0.0	0.0	0.8841	0.2830	0.0216	0.0	0.0	0.0	0.0	nan	0.3993	0.6672	0.0	0.0135	0.0076	0.0	0.0	0.0	0.0	0.0712	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.2455	0.0	0.0011	0.0000	0.0	nan	0.0	0.0	0.0	0.0	0.6484	0.2515	0.0216	0.0	0.0	0.0	0.0

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.0.0+cpu
Datasets 2.15.0
Tokenizers 0.15.0

fshala/segformer-finetuned-sidewalk-10k-steps

segformer-finetuned-sidewalk-10k-steps

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

fshala/
segformer-finetuned-sidewalk-10k-steps