metadata

license: other
base_model: nvidia/mit-b5
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5_RGB
    results: []

SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5_RGB

This model is a fine-tuned version of nvidia/mit-b5 on the Hasano20/Clean_Set1_240430_V2-Augmented dataset. It achieves the following results on the evaluation set:

Loss: 0.4812
Mean Iou: 0.6072
Mean Accuracy: 0.6905
Overall Accuracy: 0.8761
Accuracy Background: 0.8967
Accuracy Melt: 0.2439
Accuracy Substrate: 0.9311
Iou Background: 0.8325
Iou Melt: 0.1423
Iou Substrate: 0.8467

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.2826	6.6667	20	0.4812	0.6072	0.6905	0.8761	0.8967	0.2439	0.9311	0.8325	0.1423	0.8467

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1