mit-b0-finetuned-sidewalks

pecra/mit-b0-finetuned-sidewalks

mit-b0-finetuned-sidewalks is a machine learning model from pecra. 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: May 6, 2023
Updated: May 6, 2023
SHA: 3ede74ab

Model card

From the Hugging Face model README

pecra/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: 0.8276
Validation Loss: 0.7087
Validation Mean Iou: 0.2488
Validation Mean Accuracy: 0.3100
Validation Overall Accuracy: 0.7987
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.7917
Validation Accuracy Flat-sidewalk: 0.9332
Validation Accuracy Flat-crosswalk: 0.7636
Validation Accuracy Flat-cyclinglane: 0.8238
Validation Accuracy Flat-parkingdriveway: 0.1196
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.3568
Validation Accuracy Human-person: 0.7259
Validation Accuracy Human-rider: 0.0
Validation Accuracy Vehicle-car: 0.8780
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: 0.3959
Validation Accuracy Vehicle-caravan: 0.0
Validation Accuracy Vehicle-cartrailer: 0.0
Validation Accuracy Construction-building: 0.8757
Validation Accuracy Construction-door: 0.0
Validation Accuracy Construction-wall: 0.2241
Validation Accuracy Construction-fenceguardrail: 0.3394
Validation Accuracy Construction-bridge: 0.0
Validation Accuracy Construction-tunnel: 0.0
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.1857
Validation Accuracy Object-trafficsign: 0.0
Validation Accuracy Object-trafficlight: 0.0
Validation Accuracy Nature-vegetation: 0.9210
Validation Accuracy Nature-terrain: 0.7914
Validation Accuracy Sky: 0.9488
Validation Accuracy Void-ground: 0.0
Validation Accuracy Void-dynamic: 0.0
Validation Accuracy Void-static: 0.1548
Validation Accuracy Void-unclear: 0.0
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.6333
Validation Iou Flat-sidewalk: 0.8196
Validation Iou Flat-crosswalk: 0.5763
Validation Iou Flat-cyclinglane: 0.7280
Validation Iou Flat-parkingdriveway: 0.1057
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.2409
Validation Iou Human-person: 0.4810
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.7216
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: 0.2825
Validation Iou Vehicle-caravan: 0.0
Validation Iou Vehicle-cartrailer: 0.0
Validation Iou Construction-building: 0.6307
Validation Iou Construction-door: 0.0
Validation Iou Construction-wall: 0.1908
Validation Iou Construction-fenceguardrail: 0.2551
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: 0.0
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.1625
Validation Iou Object-trafficsign: 0.0
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.7648
Validation Iou Nature-terrain: 0.6071
Validation Iou Sky: 0.8958
Validation Iou Void-ground: 0.0
Validation Iou Void-dynamic: 0.0
Validation Iou Void-static: 0.1149
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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, '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 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
1.3899	0.8648	0.1967	0.2417	0.7536	0.0	0.6349	0.9590	0.1580	0.7351	0.1644	nan	0.1925	0.4170	0.0	0.8121	0.0	0.0	nan	0.0	0.1536	0.0	0.0	0.8972	0.0	0.1150	0.0695	0.0	0.0	0.0	0.0352	0.0	0.0	0.8874	0.7578	0.9500	0.0	0.0	0.0383	0.0	0.0	0.4984	0.7628	0.1568	0.6469	0.1219	nan	0.1421	0.3436	0.0	0.6648	0.0	0.0	nan	0.0	0.1348	0.0	0.0	0.5662	0.0	0.1019	0.0655	0.0	0.0	0.0	0.0345	0.0	0.0	0.7491	0.5971	0.8673	0.0	0.0	0.0366	0.0	0
0.8276	0.7087	0.2488	0.3100	0.7987	0.0	0.7917	0.9332	0.7636	0.8238	0.1196	nan	0.3568	0.7259	0.0	0.8780	0.0	0.0	nan	0.0	0.3959	0.0	0.0	0.8757	0.0	0.2241	0.3394	0.0	0.0	0.0	0.1857	0.0	0.0	0.9210	0.7914	0.9488	0.0	0.0	0.1548	0.0	0.0	0.6333	0.8196	0.5763	0.7280	0.1057	nan	0.2409	0.4810	0.0	0.7216	0.0	0.0	nan	0.0	0.2825	0.0	0.0	0.6307	0.0	0.1908	0.2551	0.0	0.0	0.0	0.1625	0.0	0.0	0.7648	0.6071	0.8958	0.0	0.0	0.1149	0.0	1

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3

Library: transformers
Type: segformer
License: other
Created: May 6, 2023
Updated: May 6, 2023