segformer_cracks

Ventalik/segformer_cracks

segformer_cracks 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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Hugging Face

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Oct 29, 2023
Updated: Nov 28, 2023
SHA: d50a7af6

Base models

nvidia/mit-b3

Model card

From the Hugging Face model README

segformer_cracks

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: 0.0499
Mean Iou: 0.7718
Mean Accuracy: 0.8317
Overall Accuracy: 0.9798
Per Category Iou: [0.9792869895386617, 0.564265846038068]
Per Category Accuracy: [0.9923313345080351, 0.671108360646227]

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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.0686	1.0	1541	0.0557	0.7541	0.8082	0.9785	[0.9779708221514636, 0.5303006858963294]	[0.9928845967047768, 0.6234677160845897]
0.049	2.0	3082	0.0527	0.7633	0.8239	0.9790	[0.9784073819168878, 0.5481400031368636]	[0.9920481107810992, 0.6557623260692017]
0.0468	3.0	4623	0.0547	0.7526	0.7996	0.9788	[0.9783360187606548, 0.5269084757862701]	[0.993975994702418, 0.6052805015161615]
0.0456	4.0	6164	0.0509	0.7677	0.8276	0.9794	[0.9788937969015667, 0.556522438909845]	[0.9922581622702671, 0.6629042271896711]
0.044	5.0	7705	0.0505	0.7678	0.8265	0.9795	[0.9789809420595871, 0.5566804258721124]	[0.9924358981457169, 0.6606494246283242]
0.0436	6.0	9246	0.0502	0.7696	0.8265	0.9798	[0.9792607857315766, 0.5598563478221208]	[0.9927329763880554, 0.6603118480646505]
0.0431	7.0	10787	0.0499	0.7718	0.8317	0.9798	[0.9792869895386617, 0.564265846038068]	[0.9923313345080351, 0.671108360646227]

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: Oct 29, 2023
Updated: Nov 28, 2023