segformer-b0-finetuned-segments-sidewalk-4

jakka/segformer-b0-finetuned-segments-sidewalk-4

segformer-b0-finetuned-segments-sidewalk-4 is a image segmentation model from jakka. 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 30, 2022
Updated: May 30, 2022
SHA: 24f1befe

Model card

From the Hugging Face model README

segformer-b0-finetuned-segments-sidewalk-4

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: 1.6258
Mean Iou: 0.1481
Mean Accuracy: 0.1991
Overall Accuracy: 0.7316
Per Category Iou: [nan, 0.4971884694242825, 0.7844619900838784, 0.0, 0.10165655377640956, 0.007428563507709108, nan, 4.566798099115959e-06, 0.0, 0.0, 0.5570746278221521, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.534278997386317, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7557693923373933, 0.5270379031768208, 0.8254522211471568, 0.0, 0.0, 0.0, 0.0]
Per Category Accuracy: [nan, 0.8698779680369205, 0.9122325676343133, 0.0, 0.10179229832932858, 0.007508413919135004, nan, 4.566798099115959e-06, 0.0, 0.0, 0.8968168359562617, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8492049383357001, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9388033874781816, 0.6627890453030717, 0.9334458854084583, 0.0, 0.0, 0.0, 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: 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
1.7912	1.0	25	1.6392	0.1412	0.1911	0.7210	[nan, 0.48942576059104514, 0.7754689525048201, 0.0, 0.031932013148008094, 0.004348266117522573, nan, 1.5527099355168697e-05, 0.0, 0.0, 0.5356571432088642, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5243044552616699, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7355207837531991, 0.4479559177066271, 0.8315839315332364, 0.0, 0.0, 0.0, 0.0]	[nan, 0.8476069713517648, 0.9129050708992534, 0.0, 0.03194435645315849, 0.004370669306327572, nan, 1.552711353699426e-05, 0.0, 0.0, 0.897824434787493, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8555478632753987, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9510113270409175, 0.5116786406550935, 0.9122706949370997, 0.0, 0.0, 0.0, 0.0]
1.7531	2.0	50	1.6258	0.1481	0.1991	0.7316	[nan, 0.4971884694242825, 0.7844619900838784, 0.0, 0.10165655377640956, 0.007428563507709108, nan, 4.566798099115959e-06, 0.0, 0.0, 0.5570746278221521, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.534278997386317, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7557693923373933, 0.5270379031768208, 0.8254522211471568, 0.0, 0.0, 0.0, 0.0]	[nan, 0.8698779680369205, 0.9122325676343133, 0.0, 0.10179229832932858, 0.007508413919135004, nan, 4.566798099115959e-06, 0.0, 0.0, 0.8968168359562617, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8492049383357001, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9388033874781816, 0.6627890453030717, 0.9334458854084583, 0.0, 0.0, 0.0, 0.0]

Framework versions

Transformers 4.19.2
Pytorch 1.11.0
Datasets 2.2.2
Tokenizers 0.12.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: apache-2.0
Created: May 30, 2022
Updated: May 30, 2022