SegFormer-B0 Open-Source Human Parsing Model - Free Deployment for Precise Identification and Segmentation of Human Body Parts in Images

Home

Segformer B0 Finetuned Human Parsing

Developed by Lexic0n

A human parsing model fine-tuned based on MIT-B0 architecture for segmenting and recognizing human body parts in images

Image Segmentation

Transformers

Open Source License:Other #Human body part segmentation #Low-parameter model #Clothing recognition

Downloads 25

Release Time : 7/30/2023

Model Overview

This model is a fine-tuned version of nvidia/mit-b0 for human parsing tasks, capable of recognizing 20 human body parts and clothing items such as hats, hair, and tops in images.

Model Features

Fine-grained human part recognition

Capable of recognizing 20 different human body parts and clothing categories

Lightweight architecture

Based on SegFormer-B0 architecture, suitable for deployment in resource-constrained environments

Transfer learning optimization

Fine-tuned on the MIT-B0 pre-trained model

Model Capabilities

Image segmentation

Human body part recognition

Clothing classification

Use Cases

Fashion analysis

Clothing attribute analysis

Automatically identify clothing types and distribution in images

Top recognition accuracy 94.05%

Virtual try-on

Human body part segmentation

Provide precise human body part segmentation for virtual try-on applications

Hair recognition accuracy 48.13%

🚀 segformer-b0-finetuned-human-parsing

This model is a fine - tuned version of [nvidia/mit - b0](https://huggingface.co/nvidia/mit - b0) on the None dataset. It's designed for image segmentation tasks in the vision field, offering specific evaluation results on the dataset.

📚 Documentation

This model achieves the following results on the evaluation set:

Property	Details
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

🔧 Technical Details

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

📄 License

The license for this model is other.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご