SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5

Hasano20/SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5

SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5 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 22, 2024
Updated: Jun 22, 2024
SHA: d2e80b73

Base models

nvidia/mit-b5

Model card

From the Hugging Face model README

SegFormer_Clean_Set1_240430_V2-Augmented_mit-b5

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: 0.0899
Mean Iou: 0.8524
Mean Accuracy: 0.8932
Overall Accuracy: 0.9653
Accuracy Background: 0.9900
Accuracy Melt: 0.7107
Accuracy Substrate: 0.9788
Iou Background: 0.9693
Iou Melt: 0.6451
Iou Substrate: 0.9429

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: 20

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
0.1394	1.6129	50	0.2486	0.6252	0.6776	0.9171	0.9800	0.0713	0.9816	0.9285	0.0680	0.8792
0.2482	3.2258	100	0.2178	0.6883	0.7470	0.9224	0.9831	0.3037	0.9543	0.9307	0.2490	0.8854
0.1697	4.8387	150	0.2044	0.6993	0.7613	0.9236	0.9847	0.3511	0.9480	0.9313	0.2796	0.8871
0.139	6.4516	200	0.1897	0.7250	0.7835	0.9317	0.9771	0.4086	0.9648	0.9415	0.3395	0.8940
0.0951	8.0645	250	0.1879	0.6863	0.7344	0.9291	0.9851	0.2414	0.9766	0.9372	0.2290	0.8928
0.0812	9.6774	300	0.1875	0.7513	0.8449	0.9285	0.9636	0.6338	0.9372	0.9370	0.4265	0.8903
0.1349	11.2903	350	0.2020	0.6825	0.7357	0.9247	0.9810	0.2577	0.9685	0.9328	0.2265	0.8882
0.1312	12.9032	400	0.1401	0.7627	0.8053	0.9465	0.9864	0.4477	0.9816	0.9624	0.4169	0.9090
0.1061	14.5161	450	0.1051	0.8297	0.8811	0.9586	0.9890	0.6853	0.9691	0.9657	0.5932	0.9302
0.0287	16.1290	500	0.1045	0.8349	0.8835	0.9598	0.9850	0.6905	0.9749	0.9640	0.6073	0.9335
0.2051	17.7419	550	0.0928	0.8466	0.8868	0.9644	0.9875	0.6906	0.9824	0.9687	0.6290	0.9420
0.0898	19.3548	600	0.0899	0.8524	0.8932	0.9653	0.9900	0.7107	0.9788	0.9693	0.6451	0.9429

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 22, 2024
Updated: Jun 22, 2024