mit-b0-finetuned-sidewalks

PieroPR/mit-b0-finetuned-sidewalks

mit-b0-finetuned-sidewalks is a machine learning model from PieroPR. 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: Dec 29, 2023
Updated: Dec 29, 2023
SHA: ca8224c2

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

PieroPR/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: 0.5658
Validation Loss: 0.6820
Validation Mean Iou: 0.2736
Validation Mean Accuracy: 0.3393
Validation Overall Accuracy: 0.8140
Validation Accuracy Unlabeled: 0.0
Validation Accuracy Flat-road: 0.8209
Validation Accuracy Flat-sidewalk: 0.9071
Validation Accuracy Flat-crosswalk: 0.8663
Validation Accuracy Flat-cyclinglane: 0.7009
Validation Accuracy Flat-parkingdriveway: 0.4370
Validation Accuracy Flat-railtrack: nan
Validation Accuracy Flat-curb: 0.4513
Validation Accuracy Human-person: 0.7244
Validation Accuracy Human-rider: 0.0
Validation Accuracy Vehicle-car: 0.9263
Validation Accuracy Vehicle-truck: 0.0
Validation Accuracy Vehicle-bus: 0.0
Validation Accuracy Vehicle-tramtrain: 0.0
Validation Accuracy Vehicle-motorcycle: 0.0
Validation Accuracy Vehicle-bicycle: 0.3297
Validation Accuracy Vehicle-caravan: 0.0
Validation Accuracy Vehicle-cartrailer: nan
Validation Accuracy Construction-building: 0.8640
Validation Accuracy Construction-door: 0.0239
Validation Accuracy Construction-wall: 0.4192
Validation Accuracy Construction-fenceguardrail: 0.3167
Validation Accuracy Construction-bridge: 0.0
Validation Accuracy Construction-tunnel: 0.0
Validation Accuracy Construction-stairs: 0.0
Validation Accuracy Object-pole: 0.3078
Validation Accuracy Object-trafficsign: 0.0000
Validation Accuracy Object-trafficlight: 0.0
Validation Accuracy Nature-vegetation: 0.9553
Validation Accuracy Nature-terrain: 0.8359
Validation Accuracy Sky: 0.9632
Validation Accuracy Void-ground: 0.0001
Validation Accuracy Void-dynamic: 0.0059
Validation Accuracy Void-static: 0.3424
Validation Accuracy Void-unclear: 0.0
Validation Iou Unlabeled: 0.0
Validation Iou Flat-road: 0.6646
Validation Iou Flat-sidewalk: 0.8269
Validation Iou Flat-crosswalk: 0.5585
Validation Iou Flat-cyclinglane: 0.6586
Validation Iou Flat-parkingdriveway: 0.2672
Validation Iou Flat-railtrack: nan
Validation Iou Flat-curb: 0.3489
Validation Iou Human-person: 0.5512
Validation Iou Human-rider: 0.0
Validation Iou Vehicle-car: 0.7427
Validation Iou Vehicle-truck: 0.0
Validation Iou Vehicle-bus: 0.0
Validation Iou Vehicle-tramtrain: 0.0
Validation Iou Vehicle-motorcycle: 0.0
Validation Iou Vehicle-bicycle: 0.2798
Validation Iou Vehicle-caravan: 0.0
Validation Iou Vehicle-cartrailer: nan
Validation Iou Construction-building: 0.7027
Validation Iou Construction-door: 0.0239
Validation Iou Construction-wall: 0.2868
Validation Iou Construction-fenceguardrail: 0.2483
Validation Iou Construction-bridge: 0.0
Validation Iou Construction-tunnel: 0.0
Validation Iou Construction-stairs: 0.0
Validation Iou Object-pole: 0.2342
Validation Iou Object-trafficsign: 0.0000
Validation Iou Object-trafficlight: 0.0
Validation Iou Nature-vegetation: 0.8203
Validation Iou Nature-terrain: 0.6535
Validation Iou Sky: 0.9096
Validation Iou Void-ground: 0.0001
Validation Iou Void-dynamic: 0.0041
Validation Iou Void-static: 0.2463
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': 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 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-bicycle	Validation Accuracy Vehicle-cartrailer	Validation Accuracy Construction-building	Validation Accuracy Construction-door	Validation Accuracy Construction-wall	Validation Accuracy Construction-fenceguardrail	Validation Accuracy Object-pole	Validation Accuracy Object-trafficsign	Validation Accuracy Nature-vegetation	Validation Accuracy Nature-terrain	Validation Accuracy Sky	Validation Accuracy Void-ground	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-bicycle	Validation Iou Vehicle-cartrailer	Validation Iou Construction-building	Validation Iou Construction-door	Validation Iou Construction-wall	Validation Iou Construction-fenceguardrail	Validation Iou Object-pole	Validation Iou Object-trafficsign	Validation Iou Nature-vegetation	Validation Iou Nature-terrain	Validation Iou Sky	Validation Iou Void-ground	Validation Iou Void-dynamic	Validation Iou Void-static	Epoch
1.4417	0.9458	0.1720	0.2164	0.7492	0.7213	0.9392	0.0032	0.6615	0.0978	nan	0.0859	0.0166	0.8492	0.0004	nan	0.8935	0.0	0.2675	0.0126	0.0139	0.0	0.9325	0.7108	0.9198	0.0	0.0	0.0155	0.5252	0.7670	0.0032	0.5555	0.0878	nan	0.0719	0.0165	0.6335	0.0004	nan	0.6036	0.0	0.1696	0.0124	0.0136	0.0	0.7625	0.5888	0.8511	0.0	0.0	0.0148	0
0.8432	0.7725	0.2355	0.2899	0.7998	0.8920	0.9053	0.6734	0.7470	0.3021	nan	0.3411	0.3125	0.9194	0.2151	nan	0.8866	0.0	0.3137	0.1988	0.1288	0.0	0.9490	0.7460	0.9398	0.0	0.0	0.0970	0.6620	0.8339	0.5670	0.5954	0.2320	nan	0.2613	0.2838	0.6686	0.1247	nan	0.6523	0.0	0.2020	0.1600	0.1165	0.0	0.7997	0.6352	0.8898	0.0	0.0	0.0860	1
0.7035	0.7581	0.2508	0.3187	0.7947	0.8945	0.8435	0.8598	0.7456	0.4480	nan	0.3224	0.5241	0.9059	0.3407	nan	0.9156	0.0	0.2807	0.2499	0.2601	0.0	0.9390	0.8018	0.9606	0.0	0.0049	0.2197	0.6317	0.8090	0.4994	0.6327	0.2302	nan	0.2613	0.4452	0.6982	0.1719	nan	0.6719	0.0	0.2417	0.1960	0.2143	0.0	0.8222	0.6768	0.8946	0.0	0.0033	0.1775	2
0.6112	0.6224	0.2727	0.3304	0.8278	0.8344	0.9527	0.8526	0.7185	0.3794	nan	0.4402	0.6559	0.9143	0.3014	nan	0.9239	0.0	0.4043	0.3335	0.2741	0.0	0.9102	0.8604	0.9556	0.0	0.0062	0.1850	0.7346	0.8435	0.7084	0.6373	0.2833	nan	0.3242	0.5212	0.7058	0.2147	nan	0.7120	0.0	0.2865	0.2758	0.2228	0.0	0.8232	0.6333	0.9095	0.0	0.0054	0.1570	3
0.5658	0.6820	0.2736	0.3393	0.8140	0.8209	0.9071	0.8663	0.7009	0.4370	nan	0.4513	0.7244	0.9263	0.3297	nan	0.8640	0.0239	0.4192	0.3167	0.3078	0.0000	0.9553	0.8359	0.9632	0.0001	0.0059	0.3424	0.6646	0.8269	0.5585	0.6586	0.2672	nan	0.3489	0.5512	0.7427	0.2798	nan	0.7027	0.0239	0.2868	0.2483	0.2342	0.0000	0.8203	0.6535	0.9096	0.0001	0.0041	0.2463	4

Framework versions

Transformers 4.35.2
TensorFlow 2.15.0
Datasets 2.16.0
Tokenizers 0.15.0

Library: transformers
Type: segformer
License: other
Created: Dec 29, 2023
Updated: Dec 29, 2023