segformer-roof

Ventalik/segformer-roof

segformer-roof is a machine learning model from Ventalik. 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.

should 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: Sep 22, 2023
Updated: May 11, 2024
SHA: 02cd1ac6

Base models

nvidia/mit-b3

Model card

From the Hugging Face model README

segformer-roof

This model is a fine-tuned version of nvidia/mit-b3 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1424
Mean Iou: 0.6570
Mean Accuracy: 0.7338
Overall Accuracy: 0.9484
Per Category Iou: [0.9476783882507867, 0.42934908622698137, 0.5939862114533478]
Per Category Accuracy: [0.9826387957018243, 0.5398301458913112, 0.6788943731278633]

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.2499	1.0	930	0.1815	0.5867	0.6662	0.9365	[0.9366364285161356, 0.31719261329477677, 0.5063121008274123]	[0.9792975820886569, 0.391370646753873, 0.6280270152281657]
0.1757	2.0	1860	0.1714	0.6022	0.6773	0.9395	[0.9393634573331426, 0.3425379991526554, 0.5248483866382888]	[0.9812229375223134, 0.428240950515148, 0.6224989524490145]
0.1594	3.0	2790	0.1629	0.6084	0.6710	0.9420	[0.9416754823459575, 0.34843136321450174, 0.5350688263682425]	[0.9853655573464773, 0.43172953399691005, 0.5959325404903174]
0.1523	4.0	3720	0.1596	0.6076	0.6748	0.9431	[0.9431751130845217, 0.3239168742399479, 0.5556505610312258]	[0.9852091404732521, 0.3708811641866297, 0.6682165559673183]
0.1439	5.0	4650	0.1523	0.6302	0.7055	0.9446	[0.9440239408727975, 0.37488313572379306, 0.5716850871490047]	[0.9823225423399531, 0.45512728622828424, 0.6790873748340986]
0.1371	6.0	5580	0.1507	0.6435	0.7255	0.9454	[0.9448592138832133, 0.40938956878632793, 0.5763842195624918]	[0.9805292644632151, 0.5269108861841263, 0.6691477422245232]
0.1353	7.0	6510	0.1483	0.6535	0.7471	0.9454	[0.945006495944485, 0.428182455906198, 0.5872114398937206]	[0.977372124880732, 0.5920333447023434, 0.6719542751488515]
0.1313	8.0	7440	0.1475	0.6543	0.7416	0.9465	[0.9458449483191159, 0.4282098022538787, 0.5888713805135868]	[0.9792916130278106, 0.5663642070609791, 0.6792639127879244]
0.1274	9.0	8370	0.1446	0.6511	0.7257	0.9477	[0.9470057563004056, 0.41812909680083715, 0.5881565577705948]	[0.983095828320569, 0.5191575508625893, 0.6747574079503997]
0.1274	10.0	9300	0.1424	0.6570	0.7338	0.9484	[0.9476783882507867, 0.42934908622698137, 0.5939862114533478]	[0.9826387957018243, 0.5398301458913112, 0.6788943731278633]

Framework versions

Transformers 4.33.1
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3

Library: transformers
Type: segformer
License: other
Created: Sep 22, 2023
Updated: May 11, 2024