segformer-b0-finetuned-segments-sidewalk-2

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

segformer-b0-finetuned-segments-sidewalk-2 is a image segmentation model from matnun. 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. --

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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Oct 12, 2022
Updated: Oct 12, 2022
SHA: ad51ea26

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: 1.9042
Mean Iou: 0.1600
Mean Accuracy: 0.1997
Overall Accuracy: 0.7338
Per Category Iou: [nan, 0.27359520957005035, 0.6563592089876799, 0.0, 0.23344374046535918, 0.0, nan, 0.0, 0.0, 0.0, 0.5539341917024321, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6213519498256361, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8012808797206368, 0.0, 0.8609473035107046, nan, 0.0, 0.0, 0.0]
Per Category Accuracy: [nan, 0.38598740280061317, 0.9344800917343116, 0.0, 0.23402267811135147, 0.0, nan, 0.0, 0.0, 0.0, 0.6574569071869553, nan, nan, nan, nan, 0.0, 0.0, nan, 0.889953470705536, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9339123774958169, 0.0, 0.9562267789312698, nan, 0.0, 0.0, 0.0]

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: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
2.8419	0.42	20	3.2243	0.1239	0.1973	0.6992	[0.0, 0.221283072298205, 0.6482498250140304, 0.0, 0.36607695456244177, 0.013827775204570018, nan, 1.0254201659129828e-05, 0.0, 0.0, 0.5416500682753081, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.5339731316050166, 0.0, 0.0006440571922786744, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7498440701547007, 0.0, 0.7659222854515146, 0.0, 0.0, 0.0, 0.0]	[nan, 0.3346613609105567, 0.8582083544770268, 0.0, 0.5101472837243907, 0.015482685970504024, nan, 1.0366454154356502e-05, 0.0, 0.0, 0.6745826026281508, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8093545247364923, 0.0, 0.0006458279514337381, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9324806212895075, 0.0, 0.797418357423677, nan, 0.0, 0.0, 0.0]
2.3662	0.83	40	2.5147	0.1402	0.1798	0.6989	[nan, 0.19549119549985344, 0.6036027201962391, 0.0, 0.0019222772099991463, 0.000300503343099692, nan, 0.0, 0.0, 0.0, 0.47853978429259575, nan, nan, nan, nan, 0.0, 0.0, nan, 0.5820555774612892, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.7898452112422248, 0.0, 0.8521568687502872, nan, 0.0, 0.0, 0.0]	[nan, 0.25107981668136076, 0.9396577375184628, 0.0, 0.0019233683746435017, 0.0003025228242666523, nan, 0.0, 0.0, 0.0, 0.5513810659584686, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8953553793561865, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9300976130892274, 0.0, 0.9250758451014455, nan, 0.0, 0.0, 0.0]
2.1745	1.25	60	2.0428	0.1485	0.1882	0.7162	[nan, 0.24240648716131, 0.6262941164542789, 0.0, 0.04440846090507781, 0.0, nan, 0.0, 0.0, 0.0, 0.522913696330921, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6194890050543631, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.7947837731119848, 0.0, 0.8609570537373858, nan, 0.0, 0.0, 0.0]	[nan, 0.3318909301752965, 0.9392945927202885, 0.0, 0.04443587164684973, 0.0, nan, 0.0, 0.0, 0.0, 0.6149676720993105, nan, nan, nan, nan, 0.0, 0.0, nan, 0.8836542113759377, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9409947331534898, 0.0, 0.9509521157666382, nan, 0.0, 0.0, 0.0]
1.986	1.67	80	1.9042	0.1600	0.1997	0.7338	[nan, 0.27359520957005035, 0.6563592089876799, 0.0, 0.23344374046535918, 0.0, nan, 0.0, 0.0, 0.0, 0.5539341917024321, nan, nan, nan, nan, 0.0, 0.0, nan, 0.6213519498256361, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8012808797206368, 0.0, 0.8609473035107046, nan, 0.0, 0.0, 0.0]	[nan, 0.38598740280061317, 0.9344800917343116, 0.0, 0.23402267811135147, 0.0, nan, 0.0, 0.0, 0.0, 0.6574569071869553, nan, nan, nan, nan, 0.0, 0.0, nan, 0.889953470705536, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.9339123774958169, 0.0, 0.9562267789312698, nan, 0.0, 0.0, 0.0]

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.5.2
Tokenizers 0.13.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Oct 12, 2022
Updated: Oct 12, 2022