segformer-b0-finetuned-segments-sidewalk-2

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

segformer-b0-finetuned-segments-sidewalk-2 is a machine learning model from Ryopper. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Nov 13, 2024
Updated: Dec 20, 2024
SHA: d6b8cfbe

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

segformer-b0-finetuned-segments-sidewalk-2

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

Loss: 1.2724
Mean Iou: 0.1495
Mean Accuracy: 0.1960
Overall Accuracy: 0.7256
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.8070
Accuracy Flat-sidewalk: 0.9392
Accuracy Flat-crosswalk: 0.0
Accuracy Flat-cyclinglane: 0.0002
Accuracy Flat-parkingdriveway: 0.0009
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.0
Accuracy Human-person: 0.0
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.8879
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: 0.0
Accuracy Vehicle-tramtrain: nan
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.0
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.8974
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.0000
Accuracy Construction-fenceguardrail: 0.0
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: 0.9038
Accuracy Nature-terrain: 0.7460
Accuracy Sky: 0.8949
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: 0.4433
Iou Flat-sidewalk: 0.7724
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.0002
Iou Flat-parkingdriveway: 0.0009
Iou Flat-railtrack: nan
Iou Flat-curb: 0.0
Iou Human-person: 0.0
Iou Human-rider: 0.0
Iou Vehicle-car: 0.6650
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
Iou Vehicle-tramtrain: nan
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.0
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.5726
Iou Construction-door: 0.0
Iou Construction-wall: 0.0000
Iou Construction-fenceguardrail: 0.0
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: 0.7664
Iou Nature-terrain: 0.5795
Iou Sky: 0.8338
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: 2
eval_batch_size: 2
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

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
1.4413	0.5	200	1.4848	0.1418	0.1882	0.7099	nan	0.7601	0.9256	0.0	0.0000	0.0008	nan	0.0	0.0	0.0	0.9123	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.8325	0.0	0.0003	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.9495	0.6045	0.8493	0.0	0.0	0.0	0.0	nan	0.4216	0.7513	0.0	0.0000	0.0008	nan	0.0	0.0	0.0	0.5842	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.5625	0.0	0.0003	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.7323	0.5296	0.8122	0.0	0.0	0.0	0.0
1.6008	1.0	400	1.2724	0.1495	0.1960	0.7256	nan	0.8070	0.9392	0.0	0.0002	0.0009	nan	0.0	0.0	0.0	0.8879	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.8974	0.0	0.0000	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.9038	0.7460	0.8949	0.0	0.0	0.0	0.0	nan	0.4433	0.7724	0.0	0.0002	0.0009	nan	0.0	0.0	0.0	0.6650	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.5726	0.0	0.0000	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.7664	0.5795	0.8338	0.0	0.0	0.0	0.0

Framework versions

Transformers 4.46.2
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Library: transformers
Type: segformer
License: other
Created: Nov 13, 2024
Updated: Dec 20, 2024