segformer-b0-finetuned-segments-sidewalk

nielsr/segformer-b0-finetuned-segments-sidewalk

segformer-b0-finetuned-segments-sidewalk is a image segmentation model from nielsr. 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 apache-2.0.

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

Downloads · 30 days

1% of all-time downloads

All-time downloads

Public

Repo size

30.4 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin15 MB · 97%

At a glance

Task: Image Segmentation
Library: transformers
License: apache-2.0
Model type: segformer
Access: Public
Created: Mar 5, 2022
Updated: Mar 5, 2022
SHA: 3805bb23

Spaces

Model card

From the Hugging Face model README

segformer-b0-finetuned-segments-sidewalk

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: 0.5679
Miou: 0.2769
Macc: 0.3331
Overall Accuracy: 0.8424
Per Category Iou: [nan, 0.7174911859423314, 0.8790751054409742, 0.6065232798410057, 0.6975274018055722, 0.3486407385349508, nan, 0.40093167116703843, 0.28779837903852556, 0.0, 0.7870339041746186, 0.0, 0.0, 0.0, 0.0, 0.1464360606454247, 0.0, 0.0, 0.6770283275082656, 0.0, 0.338555175257431, 0.14697310016578427, 0.0, nan, 0.0, 0.27163002251763635, 0.0, 0.0, 0.8257437911843676, 0.7169333376341568, 0.9108105550493353, 0.0, 0.0, 0.1016801552778885, 0.0]
Per Category Accuracy: [nan, 0.9199960254104915, 0.9327745517652714, 0.7304629327758765, 0.7378309547498484, 0.45295941407150275, nan, 0.5188608021128075, 0.5327441812670195, 0.0, 0.9353764765979435, 0.0, 0.0, 0.0, 0.0, 0.1588525415198792, 0.0, 0.0, 0.9238854794385364, 0.0, 0.4400394213522207, 0.15130051149615126, 0.0, nan, 0.0, 0.3570096986572905, 0.0, 0.0, 0.9359897980968498, 0.8570458108260572, 0.9549583230619891, 0.0, 0.0, 0.11786971668879294, 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Miou	Macc	Overall Accuracy	Per Category Iou	Per Category Accuracy
1.357	1.0	400	1.0006	0.1632	0.2069	0.7524	[nan, 0.5642795884663824, 0.7491853309192827, 0.0, 0.40589649630192104, 0.02723606910696284, nan, 0.0002207740938439576, 0.0, 0.0, 0.6632462867093903, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5671699281129761, 0.0, 0.0009207911027492868, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7507253434892517, 0.6157793573905029, 0.8774768871968204, 0.0, 0.0, 0.0, 0.0]	[nan, 0.6839993330882016, 0.9786792586618772, 0.0, 0.4818162160949784, 0.02785198456498826, nan, 0.00022133459131411787, 0.0, 0.0, 0.9043689536433023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8606078323791991, 0.0, 0.0009210330367246509, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.895198618615298, 0.8549807032886052, 0.9328734839751688, 0.0, 0.0, 0.0, 0.0]
1.6346	2.0	800	0.7856	0.1903	0.2334	0.7917	[nan, 0.6276046255936906, 0.8379492348238635, 0.0, 0.5220035981992285, 0.19441920935217594, nan, 0.16135703555333, 0.0, 0.0, 0.7357165628674137, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.567598980063164, 0.0, 0.07867871139133086, 0.0, 0.0, nan, 0.0, 0.02123705398363847, 0.0, 0.0, 0.7917172051343153, 0.6589515948064048, 0.8916684207946344, 0.0, 0.0, 0.00013685918191589503, 0.0]	[nan, 0.8610263337355926, 0.9499345560017969, 0.0, 0.5908796687797819, 0.2144081438468206, nan, 0.1813236746419022, 0.0, 0.0, 0.8825551027577866, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9239907140298015, 0.0, 0.08495225520298297, 0.0, 0.0, nan, 0.0, 0.021302829364985724, 0.0, 0.0, 0.9258397010509258, 0.8834861376443207, 0.9489131468773239, 0.0, 0.0, 0.0001372777815910495, 0.0]
0.659	3.0	1200	0.6798	0.2215	0.2687	0.8107	[nan, 0.6728474586764454, 0.8404607924530816, 0.21147709475332813, 0.5407350347311378, 0.23535489130104167, nan, 0.3087159264982809, 0.0060319580742948155, 0.0, 0.7331305064022374, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6378031991744924, 0.0, 0.35289337122777764, 6.24997656258789e-05, 0.0, nan, 0.0, 0.14698390926256938, 0.0, 0.0, 0.8019042204623998, 0.669283249725758, 0.8928145424856038, 0.0, 0.0, 0.03847722460691187, 0.0]	[nan, 0.866012011452706, 0.9627112260298595, 0.21236715482371135, 0.5645869262075475, 0.2750610095322395, nan, 0.3857655597748765, 0.0060319580742948155, 0.0, 0.939196440844118, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8380282443529743, 0.0, 0.5749902063170915, 6.256068386334744e-05, 0.0, nan, 0.0, 0.1605725590139305, 0.0, 0.0, 0.9212803460870584, 0.8870298583701837, 0.959700359744241, 0.0, 0.0, 0.04453994364914478, 0.0]
0.5481	4.0	1600	0.5999	0.2522	0.2998	0.8312	[nan, 0.7078353465279917, 0.8661728761172196, 0.3857324719136883, 0.6338278880825696, 0.3440050078187208, nan, 0.35980405625532347, 0.23875867241702606, 0.0, 0.773703347865372, 0.0, 0.0, 0.0, 0.0, 0.0004931363471679884, 0.0, 0.0, 0.6554146448850521, 0.0, 0.367673493717809, 0.03089804641909161, 0.0, nan, 0.0, 0.21529017459808872, 0.0, 0.0, 0.818951849158376, 0.7007504838794707, 0.9053929635423027, 0.0, 0.0, 0.06626212301200333, 0.0]	[nan, 0.8955207784307155, 0.9536263694097721, 0.39712577675621036, 0.6989299616008556, 0.4248959179453637, nan, 0.42984959564233455, 0.26168627652468784, 0.0, 0.9055166364779607, 0.0, 0.0, 0.0, 0.0, 0.0004932058379466533, 0.0, 0.0, 0.8632164276000204, 0.0, 0.6365580872107307, 0.031401709658368616, 0.0, nan, 0.0, 0.2497286263775161, 0.0, 0.0, 0.9296676429517725, 0.8858954297713482, 0.9555756265860916, 0.0, 0.0, 0.0750792276952902, 0.0]
0.7855	5.0	2000	0.5679	0.2769	0.3331	0.8424	[nan, 0.7174911859423314, 0.8790751054409742, 0.6065232798410057, 0.6975274018055722, 0.3486407385349508, nan, 0.40093167116703843, 0.28779837903852556, 0.0, 0.7870339041746186, 0.0, 0.0, 0.0, 0.0, 0.1464360606454247, 0.0, 0.0, 0.6770283275082656, 0.0, 0.338555175257431, 0.14697310016578427, 0.0, nan, 0.0, 0.27163002251763635, 0.0, 0.0, 0.8257437911843676, 0.7169333376341568, 0.9108105550493353, 0.0, 0.0, 0.1016801552778885, 0.0]	[nan, 0.9199960254104915, 0.9327745517652714, 0.7304629327758765, 0.7378309547498484, 0.45295941407150275, nan, 0.5188608021128075, 0.5327441812670195, 0.0, 0.9353764765979435, 0.0, 0.0, 0.0, 0.0, 0.1588525415198792, 0.0, 0.0, 0.9238854794385364, 0.0, 0.4400394213522207, 0.15130051149615126, 0.0, nan, 0.0, 0.3570096986572905, 0.0, 0.0, 0.9359897980968498, 0.8570458108260572, 0.9549583230619891, 0.0, 0.0, 0.11786971668879294, 0.0]

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.6

Task: Image Segmentation
Library: transformers
Type: segformer
License: apache-2.0
Created: Mar 5, 2022
Updated: Mar 5, 2022