segformer-b0-finetuned-segments-sidewalk-2

turcuciprian/segformer-b0-finetuned-segments-sidewalk-2

segformer-b0-finetuned-segments-sidewalk-2 is a image segmentation model from turcuciprian. Use it for the image segmentation 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.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

12% of all-time downloads

All-time downloads

Public

Repo size

60.7 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin15 MB · 100%

At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Dec 2, 2022
Updated: Mar 9, 2023
SHA: 9620e922

Model card

From the Hugging Face model README

segformer-b0-finetuned-segments-sidewalk-2

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 2.2794
Mean Iou: 0.0007
Mean Accuracy: 0.0333
Overall Accuracy: 0.0216
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.0
Accuracy Flat-sidewalk: 1.0
Accuracy Flat-crosswalk: 0.0
Accuracy Flat-cyclinglane: 0.0
Accuracy Flat-parkingdriveway: nan
Accuracy Flat-railtrack: 0.0
Accuracy Flat-curb: 0.0
Accuracy Human-person: 0.0
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.0
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: nan
Accuracy Vehicle-tramtrain: 0.0
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.0
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.0
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.0
Accuracy Construction-fenceguardrail: 0.0
Accuracy Construction-bridge: nan
Accuracy Construction-tunnel: 0.0
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.0
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.0
Accuracy Nature-terrain: 0.0
Accuracy Sky: 0.0
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.0
Accuracy Void-static: 0.0
Accuracy Void-unclear: nan
Iou Unlabeled: nan
Iou Flat-road: 0.0
Iou Flat-sidewalk: 0.0216
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.0
Iou Flat-parkingdriveway: nan
Iou Flat-railtrack: 0.0
Iou Flat-curb: 0.0
Iou Human-person: 0.0
Iou Human-rider: 0.0
Iou Vehicle-car: 0.0
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: nan
Iou Vehicle-tramtrain: 0.0
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.0
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.0
Iou Construction-door: 0.0
Iou Construction-wall: 0.0
Iou Construction-fenceguardrail: 0.0
Iou Construction-bridge: nan
Iou Construction-tunnel: 0.0
Iou Construction-stairs: 0.0
Iou Object-pole: 0.0
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.0
Iou Nature-terrain: 0.0
Iou Sky: 0.0
Iou Void-ground: 0.0
Iou Void-dynamic: 0.0
Iou Void-static: 0.0
Iou Void-unclear: nan

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:

learning_rate: 0.5
train_batch_size: 3
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-sidewalk	Accuracy Flat-parkingdriveway	Accuracy Vehicle-car	Accuracy Vehicle-bus	Accuracy Construction-bridge	Accuracy Nature-vegetation	Accuracy Sky	Accuracy Void-unclear	Iou Unlabeled	Iou Flat-sidewalk	Iou Flat-parkingdriveway	Iou Vehicle-car	Iou Vehicle-bus	Iou Construction-bridge	Iou Nature-vegetation	Iou Sky	Iou Void-unclear
1.8968	0.07	20	12.4141	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.4115	0.15	40	5.4547	0.0010	0.0310	0.0045	nan	0.0487	nan	0.2040	nan	nan	0.0027	0.6733	nan	nan	0.0218	nan	0.0036	nan	nan	0.0020	0.0035	nan
2.7593	0.22	60	2.5077	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.447	0.3	80	2.3171	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.0908	0.37	100	2.2973	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.0462	0.45	120	2.3021	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.111	0.52	140	2.3397	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.4589	0.6	160	2.3188	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.7771	0.67	180	2.2841	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.3753	0.75	200	2.2824	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.4775	0.82	220	2.2890	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.1964	0.9	240	2.2757	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan
2.1973	0.97	260	2.2794	0.0007	0.0333	0.0216	nan	1.0	nan	0.0	nan	nan	0.0	0.0	nan	nan	0.0216	nan	0.0	nan	nan	0.0	0.0	nan

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.10.1
Tokenizers 0.13.2

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Dec 2, 2022
Updated: Mar 9, 2023