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rishitunu/
ecc_segformerv3

Name: ecc_segformerv3
Availability: InStock

generated_from_trainer

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ecc_segformerv3

This model is a fine-tuned version of nvidia/mit-b5 on the rishitunu/ecc_crackdetector_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.1344
Mean Iou: 0.0005
Mean Accuracy: 0.0010
Overall Accuracy: 0.0010
Accuracy Background: nan
Accuracy Crack: 0.0010
Iou Background: 0.0
Iou Crack: 0.0010

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: 0.0006
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crack	Iou Crack
0.1306	1.0	1001	0.1114	0.0	0.0	0.0	nan	0.0	0.0
0.107	2.0	2002	0.1238	0.0000	0.0000	0.0000	nan	0.0000	0.0000
0.1285	3.0	3003	0.1631	0.0024	0.0049	0.0049	nan	0.0049	0.0048
0.0887	4.0	4004	0.1083	0.0002	0.0003	0.0003	nan	0.0003	0.0003
0.0828	5.0	5000	0.1344	0.0005	0.0010	0.0010	nan	0.0010	0.0010

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cpu
Datasets 2.14.4
Tokenizers 0.13.3

rishitunu/ecc_segformerv3

ecc_segformerv3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rishitunu/
ecc_segformerv3