segformer-b0-finetuned-human-parsing

Lexic0n/segformer-b0-finetuned-human-parsing

segformer-b0-finetuned-human-parsing is a image segmentation model from Lexic0n. 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: Jul 30, 2023
Updated: Jul 30, 2023
SHA: 4097c982

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zxy1992/Lexic0n-segformer-b0-finetuned-human-parsing

Model card

From the Hugging Face model README

segformer-b0-finetuned-human-parsing

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.9476
Mean Iou: 0.0726
Mean Accuracy: 0.1221
Overall Accuracy: 0.3575
Accuracy Background: nan
Accuracy Hat: 0.0048
Accuracy Hair: 0.4813
Accuracy Sunglasses: 0.0
Accuracy Upper-clothes: 0.9405
Accuracy Skirt: 0.0000
Accuracy Pants: 0.0631
Accuracy Dress: 0.1031
Accuracy Belt: 0.0
Accuracy Left-shoe: 0.0011
Accuracy Right-shoe: 0.0010
Accuracy Face: 0.4406
Accuracy Left-leg: 0.0291
Accuracy Right-leg: 0.0
Accuracy Left-arm: 0.0
Accuracy Right-arm: 0.0001
Accuracy Bag: 0.0114
Accuracy Scarf: 0.0
Iou Background: 0.0
Iou Hat: 0.0043
Iou Hair: 0.4221
Iou Sunglasses: 0.0
Iou Upper-clothes: 0.3239
Iou Skirt: 0.0000
Iou Pants: 0.0559
Iou Dress: 0.0728
Iou Belt: 0.0
Iou Left-shoe: 0.0011
Iou Right-shoe: 0.0009
Iou Face: 0.3872
Iou Left-leg: 0.0271
Iou Right-leg: 0.0
Iou Left-arm: 0.0
Iou Right-arm: 0.0001
Iou Bag: 0.0106
Iou Scarf: 0.0

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: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Hat	Accuracy Hair	Accuracy Sunglasses	Accuracy Upper-clothes	Accuracy Skirt	Accuracy Pants	Accuracy Dress	Accuracy Left-shoe	Accuracy Right-shoe	Accuracy Face	Accuracy Left-leg	Accuracy Right-leg	Accuracy Left-arm	Accuracy Right-arm	Accuracy Bag	Iou Hat	Iou Hair	Iou Sunglasses	Iou Upper-clothes	Iou Skirt	Iou Pants	Iou Dress	Iou Left-shoe	Iou Right-shoe	Iou Face	Iou Left-leg	Iou Right-leg	Iou Left-arm	Iou Right-arm	Iou Bag
2.5768	0.4	20	2.7812	0.0726	0.1332	0.2876	nan	0.0178	0.3204	0.0004	0.5548	0.0004	0.2555	0.2373	0.0103	0.0003	0.5637	0.0287	0.0302	0.0001	0.0008	0.2435	0.0166	0.2759	0.0001	0.2781	0.0004	0.1710	0.1295	0.0098	0.0003	0.3251	0.0260	0.0248	0.0001	0.0007	0.0491
2.2093	0.8	40	2.5166	0.0563	0.1052	0.3288	nan	0.0	0.1994	0.0	0.9447	0.0015	0.0435	0.1164	0.0008	0.0000	0.4655	0.0007	0.0003	0.0	0.0	0.0153	0.0	0.1946	0.0	0.3037	0.0015	0.0417	0.0842	0.0008	0.0000	0.3726	0.0007	0.0003	0.0	0.0	0.0124
1.8804	1.2	60	2.0209	0.0632	0.1110	0.3374	nan	0.0087	0.3724	0.0	0.9475	0.0014	0.0162	0.0528	0.0001	0.0008	0.4257	0.0561	0.0001	0.0	0.0	0.0055	0.0077	0.3472	0.0	0.3086	0.0014	0.0156	0.0403	0.0001	0.0008	0.3597	0.0515	0.0001	0.0	0.0	0.0052
1.8776	1.6	80	2.0016	0.0665	0.1154	0.3454	nan	0.0056	0.4172	0.0	0.9412	0.0000	0.0490	0.0697	0.0002	0.0006	0.4349	0.0329	0.0000	0.0	0.0000	0.0100	0.0048	0.3791	0.0	0.3138	0.0000	0.0438	0.0542	0.0002	0.0006	0.3608	0.0304	0.0000	0.0	0.0000	0.0093
1.8471	2.0	100	1.9476	0.0726	0.1221	0.3575	nan	0.0048	0.4813	0.0	0.9405	0.0000	0.0631	0.1031	0.0011	0.0010	0.4406	0.0291	0.0	0.0	0.0001	0.0114	0.0043	0.4221	0.0	0.3239	0.0000	0.0559	0.0728	0.0011	0.0009	0.3872	0.0271	0.0	0.0	0.0001	0.0106

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Jul 30, 2023
Updated: Jul 30, 2023