mit-b0-finetuned-sidewalks

guohao123/mit-b0-finetuned-sidewalks

mit-b0-finetuned-sidewalks is a machine learning model from guohao123. 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.

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

Downloads · 30 days

13% of all-time downloads

All-time downloads

Public

Repo size

546 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.h515.2 MB · 100%

At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Jul 12, 2024
Updated: Jul 12, 2024
SHA: 0d1638a8

Model card

From the Hugging Face model README

guohao123/mit-b0-finetuned-sidewalks

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

Train Loss: 2.4469
Validation Loss: 1.5292
Validation Mean Iou: 0.1261
Validation Mean Accuracy: 0.2352
Validation Overall Accuracy: 0.6917
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.4459
Validation Accuracy Flat-sidewalk: 0.8997
Validation Accuracy Flat-crosswalk: nan
Validation Accuracy Flat-cyclinglane: 0.0
Validation Accuracy Flat-parkingdriveway: 0.0484
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.0097
Validation Accuracy Human-person: 0.0001
Validation Accuracy Human-rider: nan
Validation Accuracy Vehicle-car: 0.7916
Validation Accuracy Vehicle-truck: nan
Validation Accuracy Vehicle-bus: nan
Validation Accuracy Vehicle-tramtrain: nan
Validation Accuracy Vehicle-motorcycle: nan
Validation Accuracy Vehicle-bicycle: nan
Validation Accuracy Vehicle-caravan: 0.0
Validation Accuracy Vehicle-cartrailer: nan
Validation Accuracy Construction-building: 0.4833
Validation Accuracy Construction-door: 0.0
Validation Accuracy Construction-wall: 0.0888
Validation Accuracy Construction-fenceguardrail: 0.0
Validation Accuracy Construction-bridge: nan
Validation Accuracy Construction-tunnel: nan
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.0059
Validation Accuracy Object-trafficsign: 0.0
Validation Accuracy Object-trafficlight: nan
Validation Accuracy Nature-vegetation: 0.9408
Validation Accuracy Nature-terrain: 0.6453
Validation Accuracy Sky: 0.5797
Validation Accuracy Void-ground: 0.0
Validation Accuracy Void-dynamic: nan
Validation Accuracy Void-static: 0.0
Validation Accuracy Void-unclear: nan
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.2619
Validation Iou Flat-sidewalk: 0.7620
Validation Iou Flat-crosswalk: 0.0
Validation Iou Flat-cyclinglane: 0.0
Validation Iou Flat-parkingdriveway: 0.0349
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.0086
Validation Iou Human-person: 0.0000
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.6532
Validation Iou Vehicle-truck: nan
Validation Iou Vehicle-bus: nan
Validation Iou Vehicle-tramtrain: nan
Validation Iou Vehicle-motorcycle: nan
Validation Iou Vehicle-bicycle: 0.0
Validation Iou Vehicle-caravan: 0.0
Validation Iou Vehicle-cartrailer: 0.0
Validation Iou Construction-building: 0.3892
Validation Iou Construction-door: 0.0
Validation Iou Construction-wall: 0.0390
Validation Iou Construction-fenceguardrail: 0.0
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: 0.0
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.0059
Validation Iou Object-trafficsign: 0.0
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.6633
Validation Iou Nature-terrain: 0.3991
Validation Iou Sky: 0.5664
Validation Iou Void-ground: 0.0
Validation Iou Void-dynamic: 0.0
Validation Iou Void-static: 0.0
Validation Iou Void-unclear: 0.0
Epoch: 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:

optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Validation Mean Iou	Validation Mean Accuracy	Validation Overall Accuracy	Validation Accuracy Unlabeled	Validation Accuracy Flat-road	Validation Accuracy Flat-sidewalk	Validation Accuracy Flat-crosswalk	Validation Accuracy Flat-cyclinglane	Validation Accuracy Flat-parkingdriveway	Validation Accuracy Flat-railtrack	Validation Accuracy Flat-curb	Validation Accuracy Human-person	Validation Accuracy Human-rider	Validation Accuracy Vehicle-car	Validation Accuracy Vehicle-truck	Validation Accuracy Vehicle-bus	Validation Accuracy Vehicle-tramtrain	Validation Accuracy Vehicle-motorcycle	Validation Accuracy Vehicle-bicycle	Validation Accuracy Vehicle-caravan	Validation Accuracy Vehicle-cartrailer	Validation Accuracy Construction-building	Validation Accuracy Construction-door	Validation Accuracy Construction-wall	Validation Accuracy Construction-fenceguardrail	Validation Accuracy Construction-bridge	Validation Accuracy Construction-tunnel	Validation Accuracy Construction-stairs	Validation Accuracy Object-pole	Validation Accuracy Object-trafficsign	Validation Accuracy Object-trafficlight	Validation Accuracy Nature-vegetation	Validation Accuracy Nature-terrain	Validation Accuracy Sky	Validation Accuracy Void-ground	Validation Accuracy Void-dynamic	Validation Accuracy Void-static	Validation Accuracy Void-unclear	Validation Iou Unlabeled	Validation Iou Flat-road	Validation Iou Flat-sidewalk	Validation Iou Flat-crosswalk	Validation Iou Flat-cyclinglane	Validation Iou Flat-parkingdriveway	Validation Iou Flat-railtrack	Validation Iou Flat-curb	Validation Iou Human-person	Validation Iou Human-rider	Validation Iou Vehicle-car	Validation Iou Vehicle-truck	Validation Iou Vehicle-bus	Validation Iou Vehicle-tramtrain	Validation Iou Vehicle-motorcycle	Validation Iou Vehicle-bicycle	Validation Iou Vehicle-caravan	Validation Iou Vehicle-cartrailer	Validation Iou Construction-building	Validation Iou Construction-door	Validation Iou Construction-wall	Validation Iou Construction-fenceguardrail	Validation Iou Construction-bridge	Validation Iou Construction-tunnel	Validation Iou Construction-stairs	Validation Iou Object-pole	Validation Iou Object-trafficsign	Validation Iou Object-trafficlight	Validation Iou Nature-vegetation	Validation Iou Nature-terrain	Validation Iou Sky	Validation Iou Void-ground	Validation Iou Void-dynamic	Validation Iou Void-static	Validation Iou Void-unclear	Epoch
2.4469	1.5292	0.1261	0.2352	0.6917	0.0	0.4459	0.8997	nan	0.0	0.0484	nan	0.0097	0.0001	nan	0.7916	nan	nan	nan	nan	nan	0.0	nan	0.4833	0.0	0.0888	0.0	nan	nan	0.0	0.0059	0.0	nan	0.9408	0.6453	0.5797	0.0	nan	0.0	nan	0.0	0.2619	0.7620	0.0	0.0	0.0349	nan	0.0086	0.0000	0.0	0.6532	nan	nan	nan	nan	0.0	0.0	0.0	0.3892	0.0	0.0390	0.0	0.0	0.0	0.0	0.0059	0.0	0.0	0.6633	0.3991	0.5664	0.0	0.0	0.0	0.0	0

Framework versions

Transformers 4.27.4
TensorFlow 2.6.0
Datasets 2.20.0
Tokenizers 0.13.3

Library: transformers
Type: segformer
License: other
Created: Jul 12, 2024
Updated: Jul 12, 2024