test-segformer-1

JunyaoPu/test-segformer-1

test-segformer-1 is a machine learning model from JunyaoPu. 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: May 22, 2025
Updated: Jul 22, 2025
SHA: f026f07c

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

test-segformer-1

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5184
Mean Iou: 0.5013
Mean Accuracy: 0.6080
Overall Accuracy: 0.8860
Accuracy Background: 0.9710
Accuracy Aeroplane: 0.7936
Accuracy Bicycle: 0.6422
Accuracy Bird: 0.6118
Accuracy Boat: 0.4959
Accuracy Bottle: 0.3990
Accuracy Bus: 0.7997
Accuracy Car: 0.7558
Accuracy Cat: 0.8353
Accuracy Chair: 0.3141
Accuracy Cow: 0.4212
Accuracy Diningtable: 0.2675
Accuracy Dog: 0.5355
Accuracy Horse: 0.5817
Accuracy Motorbike: 0.7442
Accuracy Person: 0.8677
Accuracy Pottedplant: 0.3351
Accuracy Sheep: 0.7845
Accuracy Sofa: 0.3684
Accuracy Train: 0.6319
Accuracy Tvmonitor: 0.6111
Iou Background: 0.9054
Iou Aeroplane: 0.7364
Iou Bicycle: 0.4610
Iou Bird: 0.4835
Iou Boat: 0.4281
Iou Bottle: 0.3470
Iou Bus: 0.7430
Iou Car: 0.6847
Iou Cat: 0.6004
Iou Chair: 0.2188
Iou Cow: 0.3697
Iou Diningtable: 0.2446
Iou Dog: 0.4173
Iou Horse: 0.3859
Iou Motorbike: 0.5811
Iou Person: 0.7178
Iou Pottedplant: 0.3016
Iou Sheep: 0.5628
Iou Sofa: 0.2973
Iou Train: 0.5734
Iou Tvmonitor: 0.4668

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 99

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Aeroplane	Accuracy Bicycle	Accuracy Bird	Accuracy Boat	Accuracy Bottle	Accuracy Bus	Accuracy Car	Accuracy Cat	Accuracy Chair	Accuracy Cow	Accuracy Diningtable	Accuracy Dog	Accuracy Horse	Accuracy Motorbike	Accuracy Person	Accuracy Pottedplant	Accuracy Sheep	Accuracy Sofa	Accuracy Train	Accuracy Tvmonitor	Iou Background	Iou Aeroplane	Iou Bicycle	Iou Bird	Iou Boat	Iou Bottle	Iou Bus	Iou Car	Iou Cat	Iou Chair	Iou Cow	Iou Diningtable	Iou Dog	Iou Horse	Iou Motorbike	Iou Person	Iou Pottedplant	Iou Sheep	Iou Sofa	Iou Train	Iou Tvmonitor
0.3686	21.7391	1000	0.5845	0.3211	0.4345	0.8352	0.9694	0.6936	0.5146	0.4319	0.4197	0.1345	0.3523	0.2277	0.6193	0.0743	0.3442	0.4724	0.5060	0.2571	0.5776	0.7439	0.2928	0.7685	0.2055	0.3511	0.1672	0.8669	0.5458	0.2152	0.3433	0.2435	0.1265	0.3481	0.1988	0.5060	0.0624	0.2761	0.3396	0.3731	0.2182	0.3127	0.6209	0.1716	0.3419	0.1634	0.3243	0.1455
0.2146	43.4783	2000	0.5172	0.4095	0.5204	0.8595	0.9684	0.6709	0.6059	0.4971	0.4196	0.2827	0.5828	0.3871	0.7104	0.2592	0.5440	0.2338	0.4815	0.3928	0.4747	0.8668	0.4723	0.7557	0.1945	0.5497	0.5785	0.8846	0.6450	0.3218	0.3542	0.3466	0.2658	0.4982	0.3832	0.5423	0.1800	0.4389	0.2254	0.3663	0.3070	0.3989	0.6469	0.2971	0.4598	0.1728	0.4736	0.3907
0.0919	65.2174	3000	0.5242	0.4735	0.5733	0.8780	0.9721	0.7582	0.5962	0.5413	0.4951	0.3764	0.8138	0.7061	0.8332	0.2066	0.2866	0.3386	0.5985	0.3335	0.6679	0.8557	0.4816	0.6698	0.2637	0.5505	0.6939	0.8981	0.7151	0.4401	0.4610	0.3947	0.3450	0.7176	0.6253	0.6174	0.1626	0.2620	0.2859	0.3921	0.2445	0.5372	0.7023	0.3574	0.5465	0.2268	0.5270	0.4854
0.0775	86.9565	4000	0.5184	0.5013	0.6080	0.8860	0.9710	0.7936	0.6422	0.6118	0.4959	0.3990	0.7997	0.7558	0.8353	0.3141	0.4212	0.2675	0.5355	0.5817	0.7442	0.8677	0.3351	0.7845	0.3684	0.6319	0.6111	0.9054	0.7364	0.4610	0.4835	0.4281	0.3470	0.7430	0.6847	0.6004	0.2188	0.3697	0.2446	0.4173	0.3859	0.5811	0.7178	0.3016	0.5628	0.2973	0.5734	0.4668

Framework versions

Transformers 4.44.2
Pytorch 2.3.0
Datasets 3.0.0
Tokenizers 0.19.1

Library: transformers
Type: segformer
License: other
Created: May 22, 2025
Updated: Jul 22, 2025