SegFormer_Clean_Set1_Grayscale_mit-b5_Grayscale

Hasano20/SegFormer_Clean_Set1_Grayscale_mit-b5_Grayscale

SegFormer_Clean_Set1_Grayscale_mit-b5_Grayscale is a image segmentation model from Hasano20. 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: Jun 24, 2024
Updated: Jun 25, 2024
SHA: 4db97bd4

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_Clean_Set1_Grayscale_mit-b5_Grayscale

This model is a fine-tuned version of nvidia/mit-b5 on the Hasano20/Clean_Set1_Grayscale dataset. It achieves the following results on the evaluation set:

Loss: 0.0178
Mean Iou: 0.9760
Mean Accuracy: 0.9847
Overall Accuracy: 0.9949
Accuracy Background: 0.9976
Accuracy Melt: 0.9586
Accuracy Substrate: 0.9978
Iou Background: 0.9959
Iou Melt: 0.9408
Iou Substrate: 0.9912

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 50

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
0.0882	5.5556	50	0.1733	0.7543	0.7985	0.9397	0.9594	0.4375	0.9986	0.9562	0.4189	0.8880
0.0295	11.1111	100	0.0270	0.9580	0.9736	0.9907	0.9965	0.9302	0.9940	0.9918	0.8978	0.9843
0.0143	16.6667	150	0.0260	0.9561	0.9798	0.9901	0.9970	0.9541	0.9884	0.9910	0.8938	0.9836
0.0095	22.2222	200	0.0224	0.9645	0.9747	0.9926	0.9985	0.9293	0.9962	0.9944	0.9119	0.9872
0.0083	27.7778	250	0.0180	0.9742	0.9819	0.9945	0.9982	0.9498	0.9977	0.9955	0.9366	0.9905
0.0072	33.3333	300	0.0175	0.9751	0.9838	0.9947	0.9984	0.9563	0.9968	0.9957	0.9388	0.9909
0.0073	38.8889	350	0.0177	0.9758	0.9854	0.9948	0.9970	0.9613	0.9978	0.9957	0.9406	0.9912
0.0054	44.4444	400	0.0179	0.9758	0.9844	0.9949	0.9978	0.9579	0.9976	0.9959	0.9404	0.9911
0.0052	50.0	450	0.0178	0.9760	0.9847	0.9949	0.9976	0.9586	0.9978	0.9959	0.9408	0.9912

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Jun 24, 2024
Updated: Jun 25, 2024