mit-b0-finetuned-sidewalks

Champpatcharapon/mit-b0-finetuned-sidewalks

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

19% of all-time downloads

All-time downloads

Public

Repo size

91 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: Jan 10, 2024
Updated: Jan 10, 2024
SHA: 3dea5bd2

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

Champpatcharapon/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: 0.5494
Validation Loss: 0.5835
Validation Mean Iou: 0.2888
Validation Mean Accuracy: 0.3393
Validation Overall Accuracy: 0.8396
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.8572
Validation Accuracy Flat-sidewalk: 0.9633
Validation Accuracy Flat-crosswalk: 0.7607
Validation Accuracy Flat-cyclinglane: 0.8014
Validation Accuracy Flat-parkingdriveway: 0.3500
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.3332
Validation Accuracy Human-person: 0.6159
Validation Accuracy Human-rider: 0.0
Validation Accuracy Vehicle-car: 0.9313
Validation Accuracy Vehicle-truck: 0.0
Validation Accuracy Vehicle-bus: nan
Validation Accuracy Vehicle-tramtrain: 0.0
Validation Accuracy Vehicle-motorcycle: 0.0
Validation Accuracy Vehicle-bicycle: 0.6252
Validation Accuracy Vehicle-caravan: 0.0
Validation Accuracy Vehicle-cartrailer: 0.0
Validation Accuracy Construction-building: 0.9245
Validation Accuracy Construction-door: 0.0
Validation Accuracy Construction-wall: 0.4053
Validation Accuracy Construction-fenceguardrail: 0.5600
Validation Accuracy Construction-bridge: 0.0
Validation Accuracy Construction-tunnel: 0.0
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.1322
Validation Accuracy Object-trafficsign: 0.0
Validation Accuracy Object-trafficlight: 0.0
Validation Accuracy Nature-vegetation: 0.8844
Validation Accuracy Nature-terrain: 0.8885
Validation Accuracy Sky: 0.9475
Validation Accuracy Void-ground: 0.0
Validation Accuracy Void-dynamic: 0.0479
Validation Accuracy Void-static: 0.1690
Validation Accuracy Void-unclear: 0.0
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.7561
Validation Iou Flat-sidewalk: 0.8481
Validation Iou Flat-crosswalk: 0.6612
Validation Iou Flat-cyclinglane: 0.7653
Validation Iou Flat-parkingdriveway: 0.2915
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.2777
Validation Iou Human-person: 0.4287
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.8148
Validation Iou Vehicle-truck: 0.0
Validation Iou Vehicle-bus: nan
Validation Iou Vehicle-tramtrain: 0.0
Validation Iou Vehicle-motorcycle: 0.0
Validation Iou Vehicle-bicycle: 0.5016
Validation Iou Vehicle-caravan: 0.0
Validation Iou Vehicle-cartrailer: 0.0
Validation Iou Construction-building: 0.6742
Validation Iou Construction-door: 0.0
Validation Iou Construction-wall: 0.3287
Validation Iou Construction-fenceguardrail: 0.5116
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: 0.0
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.1084
Validation Iou Object-trafficsign: 0.0
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.8004
Validation Iou Nature-terrain: 0.6783
Validation Iou Sky: 0.9032
Validation Iou Void-ground: 0.0
Validation Iou Void-dynamic: 0.0383
Validation Iou Void-static: 0.1408
Validation Iou Void-unclear: 0.0
Epoch: 4

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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, '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 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 Vehicle-car	Validation Accuracy Vehicle-bus	Validation Accuracy Vehicle-bicycle	Validation Accuracy Construction-building	Validation Accuracy Construction-wall	Validation Accuracy Construction-fenceguardrail	Validation Accuracy Object-pole	Validation Accuracy Nature-vegetation	Validation Accuracy Nature-terrain	Validation Accuracy Sky	Validation Accuracy Void-dynamic	Validation Accuracy Void-static	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 Vehicle-car	Validation Iou Vehicle-bus	Validation Iou Vehicle-bicycle	Validation Iou Construction-building	Validation Iou Construction-wall	Validation Iou Construction-fenceguardrail	Validation Iou Object-pole	Validation Iou Nature-vegetation	Validation Iou Nature-terrain	Validation Iou Sky	Validation Iou Void-dynamic	Validation Iou Void-static	Epoch
1.4116	0.8269	0.2019	0.2476	0.7727	0.8916	0.9016	0.6978	0.5716	0.0894	nan	0.0778	0.1019	0.8707	nan	0.0	0.8440	0.3358	0.1615	0.0068	0.9127	0.7984	0.9066	0.0026	0.0012	0.6132	0.7821	0.5796	0.5333	0.0836	nan	0.0737	0.0945	0.7103	nan	0.0	0.5881	0.2505	0.1543	0.0067	0.7165	0.6349	0.8368	0.0025	0.0012	0
0.8301	0.6820	0.2489	0.3016	0.8118	0.8773	0.9216	0.7371	0.8211	0.2570	nan	0.3099	0.4436	0.9227	nan	0.1039	0.8900	0.4814	0.3454	0.0618	0.8614	0.8955	0.9402	0.0347	0.0489	0.7130	0.8287	0.6288	0.7403	0.1962	nan	0.2305	0.3088	0.7449	nan	0.1027	0.6333	0.3670	0.3165	0.0543	0.7655	0.6439	0.8644	0.0285	0.0449	1
0.6911	0.6086	0.2695	0.3260	0.8286	0.8834	0.9458	0.7801	0.6782	0.3498	nan	0.4269	0.5345	0.9211	nan	0.3781	0.8925	0.4993	0.4678	0.1311	0.8731	0.8832	0.9546	0.0216	0.1380	0.7421	0.8535	0.5164	0.6553	0.2701	nan	0.3176	0.3743	0.7812	nan	0.3588	0.6587	0.3759	0.4327	0.0963	0.7928	0.6585	0.8808	0.0160	0.1136	2
0.6047	0.6020	0.2791	0.3313	0.8352	0.9100	0.9415	0.7045	0.7803	0.3562	nan	0.3986	0.6625	0.9405	nan	0.5650	0.9075	0.3878	0.4329	0.1112	0.9022	0.8379	0.9341	0.0400	0.1213	0.7564	0.8530	0.6112	0.7482	0.2809	nan	0.3122	0.4085	0.7955	nan	0.4589	0.6679	0.3061	0.4204	0.0935	0.7906	0.6730	0.8976	0.0327	0.1037	3
0.5494	0.5835	0.2888	0.3393	0.8396	0.8572	0.9633	0.7607	0.8014	0.3500	nan	0.3332	0.6159	0.9313	nan	0.6252	0.9245	0.4053	0.5600	0.1322	0.8844	0.8885	0.9475	0.0479	0.1690	0.7561	0.8481	0.6612	0.7653	0.2915	nan	0.2777	0.4287	0.8148	nan	0.5016	0.6742	0.3287	0.5116	0.1084	0.8004	0.6783	0.9032	0.0383	0.1408	4

Framework versions

Transformers 4.36.2
TensorFlow 2.15.0
Datasets 2.16.1
Tokenizers 0.15.0

Library: transformers
Type: segformer
License: other
Created: Jan 10, 2024
Updated: Jan 10, 2024