segformer-b0-pavement

reannayang/segformer-b0-pavement

segformer-b0-pavement is a image segmentation model from reannayang. 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. --

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At a glance

Task: Image Segmentation
Library: transformers
License: apache-2.0
Model type: segformer
Access: Public
Created: May 23, 2022
Updated: May 23, 2022
SHA: 2c3ba1fb

Model card

From the Hugging Face model README

segformer-b0-pavement

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

Loss: 0.4165
Mean Iou: 0.6318
Mean Accuracy: 0.9700
Overall Accuracy: 0.9738
Per Category Iou: [0.0, 0.964166382973358, 0.9809231860559384, 0.0, 0.9295139919583345, 0.9164463823409184]
Per Category Accuracy: [nan, 0.9643001261034048, 0.9983497924348297, nan, 0.995031342981772, 0.9223532638507954]

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
1.0651	10.0	20	1.3005	0.5967	0.9512	0.9534	[0.0, 0.9462421185372005, 0.9681701711239586, 0.0, 0.7994398965962947, 0.8662896799897185]	[nan, 0.9462421185372005, 0.9693809143181291, nan, 0.9648149753011526, 0.9243828853538124]
0.5732	20.0	40	0.6626	0.6287	0.9702	0.9760	[0.0, 0.975246652572234, 0.985446932366533, 0.0, 0.9010974339804011, 0.9103918683964157]	[nan, 0.9772635561160151, 0.9952040842637238, nan, 0.9748678395008233, 0.9334887547997806]
0.6987	30.0	60	0.4319	0.6317	0.9705	0.9758	[0.0, 0.9709705045212967, 0.9798115236227942, 0.0, 0.9255918522130127, 0.9139245313729214]	[nan, 0.9722194199243379, 0.9986205296134905, nan, 0.9871161568015715, 0.924026330224904]
0.6915	40.0	80	0.4382	0.6237	0.9634	0.9692	[0.0, 0.9611727616645649, 0.9725125142706595, 0.0, 0.9147983251179308, 0.8937433316006894]	[nan, 0.9611727616645649, 0.9993811721630611, nan, 0.9971690210012422, 0.896023038946791]
0.4373	50.0	100	0.4165	0.6318	0.9700	0.9738	[0.0, 0.964166382973358, 0.9809231860559384, 0.0, 0.9295139919583345, 0.9164463823409184]	[nan, 0.9643001261034048, 0.9983497924348297, nan, 0.995031342981772, 0.9223532638507954]

Framework versions

Transformers 4.19.2
Pytorch 1.7.1
Datasets 2.2.1
Tokenizers 0.12.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: apache-2.0
Created: May 23, 2022
Updated: May 23, 2022