mit-b0-finetuned-sidewalks

diegola123/mit-b0-finetuned-sidewalks

mit-b0-finetuned-sidewalks is a machine learning model from diegola123. 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: Apr 8, 2024
Updated: Apr 8, 2024
SHA: ab6bfdc7

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

diegola123/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: 0.9385
Validation Loss: 0.8219
Validation Mean Iou: 0.1978
Validation Mean Accuracy: 0.2443
Validation Overall Accuracy: 0.7617
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.7657
Validation Accuracy Flat-sidewalk: 0.9179
Validation Accuracy Flat-crosswalk: 0.4156
Validation Accuracy Flat-cyclinglane: 0.6428
Validation Accuracy Flat-parkingdriveway: 0.3116
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.1868
Validation Accuracy Human-person: 0.0310
Validation Accuracy Human-rider: 0.0
Validation Accuracy Vehicle-car: 0.8157
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: 0.0
Validation Accuracy Vehicle-caravan: nan
Validation Accuracy Vehicle-cartrailer: 0.0
Validation Accuracy Construction-building: 0.8090
Validation Accuracy Construction-door: 0.0
Validation Accuracy Construction-wall: 0.2585
Validation Accuracy Construction-fenceguardrail: 0.1117
Validation Accuracy Construction-bridge: 0.0
Validation Accuracy Construction-tunnel: 0.0
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.1427
Validation Accuracy Object-trafficsign: 0.0009
Validation Accuracy Object-trafficlight: 0.0
Validation Accuracy Nature-vegetation: 0.9491
Validation Accuracy Nature-terrain: 0.6740
Validation Accuracy Sky: 0.7821
Validation Accuracy Void-ground: 0.0
Validation Accuracy Void-dynamic: 0.0
Validation Accuracy Void-static: 0.0037
Validation Accuracy Void-unclear: 0.0
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.5953
Validation Iou Flat-sidewalk: 0.7737
Validation Iou Flat-crosswalk: 0.3640
Validation Iou Flat-cyclinglane: 0.5889
Validation Iou Flat-parkingdriveway: 0.1813
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.1674
Validation Iou Human-person: 0.0300
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.5407
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: 0.0
Validation Iou Vehicle-caravan: nan
Validation Iou Vehicle-cartrailer: 0.0
Validation Iou Construction-building: 0.5474
Validation Iou Construction-door: 0.0
Validation Iou Construction-wall: 0.2017
Validation Iou Construction-fenceguardrail: 0.0897
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: 0.0
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.1280
Validation Iou Object-trafficsign: 0.0009
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.7228
Validation Iou Nature-terrain: 0.6364
Validation Iou Sky: 0.7594
Validation Iou Void-ground: 0.0
Validation Iou Void-dynamic: 0.0
Validation Iou Void-static: 0.0036
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': 0.001, '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
1.2568	1.0277	0.1717	0.2305	0.7089	0.0	0.7565	0.8215	0.6421	0.4804	0.1174	nan	0.2548	0.0847	0.0	0.7733	0.0	0.0	nan	0.0	0.0	nan	0.0	0.6221	0.0	0.1317	0.0002	0.0	0.0	0.0	0.0988	0.0	0.0	0.9727	0.6553	0.9463	0.0	0.0	0.0173	0.0	0.0	0.4869	0.7244	0.3171	0.4769	0.0943	nan	0.1866	0.0608	0.0	0.4146	0.0	0.0	nan	0.0	0.0	nan	0.0	0.4891	0.0	0.1186	0.0002	0.0	0.0	0.0	0.0939	0.0	0.0	0.6589	0.5986	0.7578	0.0	0.0	0.0160	0.0	0
0.9385	0.8219	0.1978	0.2443	0.7617	0.0	0.7657	0.9179	0.4156	0.6428	0.3116	nan	0.1868	0.0310	0.0	0.8157	0.0	0.0	nan	0.0	0.0	nan	0.0	0.8090	0.0	0.2585	0.1117	0.0	0.0	0.0	0.1427	0.0009	0.0	0.9491	0.6740	0.7821	0.0	0.0	0.0037	0.0	0.0	0.5953	0.7737	0.3640	0.5889	0.1813	nan	0.1674	0.0300	0.0	0.5407	0.0	0.0	nan	0.0	0.0	nan	0.0	0.5474	0.0	0.2017	0.0897	0.0	0.0	0.0	0.1280	0.0009	0.0	0.7228	0.6364	0.7594	0.0	0.0	0.0036	0.0	1

Framework versions

Transformers 4.38.2
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2

Library: transformers
Type: segformer
License: other
Created: Apr 8, 2024
Updated: Apr 8, 2024