segformer-b0-finetuned-segments-sidewalk-oct-22

maratuly/segformer-b0-finetuned-segments-sidewalk-oct-22

segformer-b0-finetuned-segments-sidewalk-oct-22 is a image segmentation model from maratuly. 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: Nov 24, 2023
Updated: Nov 24, 2023
SHA: ea4b644a

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

segformer-b0-finetuned-segments-sidewalk-oct-22

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

Loss: 0.3463
Mean Iou: 0.7622
Mean Accuracy: 0.9528
Overall Accuracy: 0.9581
Accuracy Unlabeled: nan
Accuracy Lv: 0.9931
Accuracy Rv: 0.9354
Accuracy La: 0.9533
Accuracy Ra: 0.9293
Iou Unlabeled: 0.0
Iou Lv: 0.9931
Iou Rv: 0.9354
Iou La: 0.9533
Iou Ra: 0.9293

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	Accuracy Unlabeled	Accuracy Lv	Accuracy Rv	Accuracy La	Accuracy Ra	Iou Lv	Iou Rv	Iou La	Iou Ra
1.0046	5.0	20	1.3540	0.4931	0.7263	0.7606	nan	0.9742	0.6789	0.5862	0.6657	0.6545	0.6789	0.5540	0.5783
0.7304	10.0	40	0.8262	0.6901	0.8908	0.9017	nan	0.9906	0.8147	0.9192	0.8387	0.9028	0.8145	0.9046	0.8287
0.5957	15.0	60	0.5683	0.7395	0.9277	0.9367	nan	0.9896	0.9141	0.9225	0.8845	0.9766	0.9141	0.9225	0.8845
0.5094	20.0	80	0.4881	0.7541	0.9426	0.9499	nan	0.9909	0.9368	0.9312	0.9116	0.9909	0.9368	0.9312	0.9116
0.4681	25.0	100	0.4384	0.7689	0.9612	0.9653	nan	0.9942	0.9434	0.9650	0.9421	0.9942	0.9434	0.9650	0.9421
0.4045	30.0	120	0.4078	0.7639	0.9549	0.9602	nan	0.9933	0.9418	0.9519	0.9328	0.9933	0.9418	0.9519	0.9328
0.3956	35.0	140	0.3844	0.7664	0.9580	0.9625	nan	0.9939	0.9406	0.9551	0.9425	0.9939	0.9406	0.9551	0.9425
0.3736	40.0	160	0.3736	0.7687	0.9609	0.9652	nan	0.9961	0.9409	0.9631	0.9436	0.9961	0.9409	0.9631	0.9436
0.3431	45.0	180	0.3528	0.7622	0.9528	0.9577	nan	0.9923	0.9321	0.9539	0.9327	0.9923	0.9321	0.9539	0.9327
0.3428	50.0	200	0.3463	0.7622	0.9528	0.9581	nan	0.9931	0.9354	0.9533	0.9293	0.9931	0.9354	0.9533	0.9293

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Nov 24, 2023
Updated: Nov 24, 2023