mit-b0-finetuned-sidewalks

MariaK/mit-b0-finetuned-sidewalks

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

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Jan 20, 2023
Updated: Jan 20, 2023
SHA: 171abfe5

Model card

From the Hugging Face model README

MariaK/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: 0.8550
Validation Loss: 0.8639
Validation Mean Iou: 0.2220
Validation Mean Accuracy: 0.2670
Validation Overall Accuracy: 0.7725
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.6015
Validation Accuracy Flat-sidewalk: 0.9708
Validation Accuracy Flat-crosswalk: 0.3807
Validation Accuracy Flat-cyclinglane: 0.7538
Validation Accuracy Flat-parkingdriveway: 0.1524
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.1957
Validation Accuracy Human-person: 0.2585
Validation Accuracy Human-rider: 0.0
Validation Accuracy Vehicle-car: 0.8971
Validation Accuracy Vehicle-truck: 0.0
Validation Accuracy Vehicle-bus: 0.0
Validation Accuracy Vehicle-tramtrain: nan
Validation Accuracy Vehicle-motorcycle: 0.0
Validation Accuracy Vehicle-bicycle: 0.0716
Validation Accuracy Vehicle-caravan: 0.0
Validation Accuracy Vehicle-cartrailer: 0.0
Validation Accuracy Construction-building: 0.8784
Validation Accuracy Construction-door: 0.0
Validation Accuracy Construction-wall: 0.4315
Validation Accuracy Construction-fenceguardrail: 0.1948
Validation Accuracy Construction-bridge: 0.0
Validation Accuracy Construction-tunnel: nan
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.1201
Validation Accuracy Object-trafficsign: 0.0
Validation Accuracy Object-trafficlight: 0.0
Validation Accuracy Nature-vegetation: 0.8952
Validation Accuracy Nature-terrain: 0.8231
Validation Accuracy Sky: 0.8496
Validation Accuracy Void-ground: 0.0
Validation Accuracy Void-dynamic: 0.0
Validation Accuracy Void-static: 0.0692
Validation Accuracy Void-unclear: 0.0
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.5568
Validation Iou Flat-sidewalk: 0.7479
Validation Iou Flat-crosswalk: 0.3509
Validation Iou Flat-cyclinglane: 0.6355
Validation Iou Flat-parkingdriveway: 0.1298
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.1326
Validation Iou Human-person: 0.2455
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.6973
Validation Iou Vehicle-truck: 0.0
Validation Iou Vehicle-bus: 0.0
Validation Iou Vehicle-tramtrain: nan
Validation Iou Vehicle-motorcycle: 0.0
Validation Iou Vehicle-bicycle: 0.0610
Validation Iou Vehicle-caravan: 0.0
Validation Iou Vehicle-cartrailer: 0.0
Validation Iou Construction-building: 0.6479
Validation Iou Construction-door: 0.0
Validation Iou Construction-wall: 0.3003
Validation Iou Construction-fenceguardrail: 0.1727
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: nan
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.0927
Validation Iou Object-trafficsign: 0.0
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.7758
Validation Iou Nature-terrain: 0.7000
Validation Iou Sky: 0.8002
Validation Iou Void-ground: 0.0
Validation Iou Void-dynamic: 0.0
Validation Iou Void-static: 0.0573
Validation Iou Void-unclear: 0.0
Epoch: 1

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
1.4362	0.9804	0.1752	0.2219	0.7360	0.0	0.7417	0.9512	0.0213	0.3662	0.1475	nan	0.1397	0.0055	0.0	0.8653	0.0	0.0	nan	0.0	0.0002	0.0	0.0	0.7778	0.0	0.3370	0.0429	0.0	nan	0.0	0.0177	0.0	0.0	0.9324	0.7967	0.9157	0.0	0.0	0.0409	0.0	0.0	0.5263	0.7377	0.0213	0.3517	0.1232	nan	0.1053	0.0055	0.0	0.6423	0.0	0.0	nan	0.0	0.0002	0.0	0.0	0.6012	0.0	0.2315	0.0424	0.0	nan	0.0	0.0163	0.0	0.0	0.7258	0.6752	0.7692	0.0	0.0	0.0321	0.0	0
0.8550	0.8639	0.2220	0.2670	0.7725	0.0	0.6015	0.9708	0.3807	0.7538	0.1524	nan	0.1957	0.2585	0.0	0.8971	0.0	0.0	nan	0.0	0.0716	0.0	0.0	0.8784	0.0	0.4315	0.1948	0.0	nan	0.0	0.1201	0.0	0.0	0.8952	0.8231	0.8496	0.0	0.0	0.0692	0.0	0.0	0.5568	0.7479	0.3509	0.6355	0.1298	nan	0.1326	0.2455	0.0	0.6973	0.0	0.0	nan	0.0	0.0610	0.0	0.0	0.6479	0.0	0.3003	0.1727	0.0	nan	0.0	0.0927	0.0	0.0	0.7758	0.7000	0.8002	0.0	0.0	0.0573	0.0	1

Framework versions

Transformers 4.25.1
TensorFlow 2.9.2
Datasets 2.8.0
Tokenizers 0.13.2

Library: transformers
Type: segformer
License: other
Created: Jan 20, 2023
Updated: Jan 20, 2023