mit-b0-finetuned-human-parsing-dataset

raks87/mit-b0-finetuned-human-parsing-dataset

mit-b0-finetuned-human-parsing-dataset is a machine learning model from raks87. Use it for the machine learning 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

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Apr 13, 2024
Updated: Apr 14, 2024
SHA: 70b934a4

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

mit-b0-finetuned-human-parsing-dataset

This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1612
Mean Iou: 0.5450
Mean Accuracy: 0.6607
Overall Accuracy: 0.8160
Accuracy Background: nan
Accuracy Hat: 0.5935
Accuracy Hair: 0.8675
Accuracy Sunglasses: 0.1278
Accuracy Upper-clothes: 0.8806
Accuracy Skirt: 0.7150
Accuracy Pants: 0.8529
Accuracy Dress: 0.8186
Accuracy Belt: 0.0817
Accuracy Left-shoe: 0.6562
Accuracy Right-shoe: 0.6193
Accuracy Face: 0.8987
Accuracy Left-leg: 0.8838
Accuracy Right-leg: 0.8541
Accuracy Left-arm: 0.8193
Accuracy Right-arm: 0.8202
Accuracy Bag: 0.7409
Accuracy Scarf: 0.0012
Iou Background: 0.0
Iou Hat: 0.5417
Iou Hair: 0.7745
Iou Sunglasses: 0.1273
Iou Upper-clothes: 0.7733
Iou Skirt: 0.6469
Iou Pants: 0.7596
Iou Dress: 0.6192
Iou Belt: 0.0773
Iou Left-shoe: 0.5307
Iou Right-shoe: 0.5156
Iou Face: 0.8002
Iou Left-leg: 0.7577
Iou Right-leg: 0.7632
Iou Left-arm: 0.7325
Iou Right-arm: 0.7315
Iou Bag: 0.6578
Iou Scarf: 0.0012

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: 7e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
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 Hat	Accuracy Hair	Accuracy Sunglasses	Accuracy Upper-clothes	Accuracy Skirt	Accuracy Pants	Accuracy Dress	Accuracy Belt	Accuracy Left-shoe	Accuracy Right-shoe	Accuracy Face	Accuracy Left-leg	Accuracy Right-leg	Accuracy Left-arm	Accuracy Right-arm	Accuracy Bag	Accuracy Scarf	Iou Hat	Iou Hair	Iou Sunglasses	Iou Upper-clothes	Iou Skirt	Iou Pants	Iou Dress	Iou Belt	Iou Left-shoe	Iou Right-shoe	Iou Face	Iou Left-leg	Iou Right-leg	Iou Left-arm	Iou Right-arm	Iou Bag	Iou Scarf
0.1738	1.0	200	0.2036	0.4669	0.5844	0.7641	nan	0.2353	0.8366	0.0	0.8116	0.6698	0.7972	0.8264	0.0	0.5317	0.4734	0.8657	0.8419	0.7916	0.7738	0.7692	0.7112	0.0	0.2283	0.7279	0.0	0.7127	0.5858	0.7043	0.5625	0.0	0.4160	0.3786	0.7652	0.6935	0.6938	0.6787	0.6674	0.5891	0.0
0.184	2.0	400	0.1841	0.4970	0.6199	0.7940	nan	0.4453	0.8607	0.0	0.8745	0.7569	0.8160	0.7201	0.0	0.5756	0.5385	0.9054	0.8440	0.8553	0.8249	0.8242	0.6971	0.0	0.4153	0.7599	0.0	0.7467	0.6348	0.7162	0.5777	0.0	0.4517	0.4229	0.7710	0.7025	0.7143	0.7096	0.7060	0.6183	0.0
0.1793	3.0	600	0.1717	0.5121	0.6276	0.8018	nan	0.5648	0.8591	0.0	0.8920	0.7414	0.8757	0.7207	0.0	0.6178	0.5797	0.8696	0.8117	0.8442	0.7867	0.7884	0.7170	0.0	0.4805	0.7622	0.0	0.7497	0.6576	0.7450	0.5970	0.0	0.4793	0.4538	0.7821	0.7290	0.7402	0.7075	0.7070	0.6269	0.0
0.3023	4.0	800	0.1753	0.5129	0.6313	0.7953	nan	0.5461	0.8778	0.0	0.8113	0.7911	0.8080	0.8468	0.0	0.6061	0.5468	0.8959	0.8538	0.8359	0.8053	0.8009	0.7055	0.0	0.4921	0.7589	0.0	0.7408	0.6533	0.7325	0.5989	0.0	0.4843	0.4550	0.7872	0.7399	0.7417	0.7114	0.7089	0.6265	0.0
0.1041	5.0	1000	0.1655	0.5235	0.6388	0.8078	nan	0.6147	0.8667	0.0025	0.8768	0.7477	0.8536	0.7777	0.0022	0.5801	0.5814	0.8896	0.8580	0.8658	0.8238	0.8236	0.6964	0.0	0.5389	0.7662	0.0025	0.7582	0.6485	0.7581	0.6070	0.0022	0.4840	0.4767	0.7900	0.7534	0.7572	0.7267	0.7204	0.6336	0.0
0.1179	6.0	1200	0.1628	0.5312	0.6475	0.8111	nan	0.5886	0.8725	0.0326	0.8560	0.7353	0.8538	0.8384	0.0221	0.6322	0.5871	0.9038	0.8580	0.8579	0.8263	0.8279	0.7142	0.0	0.5293	0.7663	0.0326	0.7629	0.6531	0.7624	0.6189	0.0217	0.5135	0.4931	0.7930	0.7599	0.7641	0.7293	0.7224	0.6386	0.0
0.1323	7.0	1400	0.1619	0.5390	0.6531	0.8129	nan	0.6147	0.8846	0.0754	0.8677	0.7143	0.8672	0.8331	0.0484	0.6528	0.6319	0.8896	0.8392	0.8467	0.8096	0.8072	0.7201	0.0	0.5489	0.7726	0.0753	0.7693	0.6392	0.7660	0.6163	0.0468	0.5264	0.5172	0.7976	0.7612	0.7666	0.7314	0.7245	0.6434	0.0
0.1235	8.0	1600	0.1612	0.5450	0.6607	0.8160	nan	0.5935	0.8675	0.1278	0.8806	0.7150	0.8529	0.8186	0.0817	0.6562	0.6193	0.8987	0.8838	0.8541	0.8193	0.8202	0.7409	0.0012	0.5417	0.7745	0.1273	0.7733	0.6469	0.7596	0.6192	0.0773	0.5307	0.5156	0.8002	0.7577	0.7632	0.7325	0.7315	0.6578	0.0012
0.093	9.0	1800	0.1621	0.5489	0.6621	0.8165	nan	0.6190	0.8752	0.1554	0.8799	0.7153	0.8561	0.8333	0.0834	0.6491	0.6138	0.8950	0.8622	0.8528	0.8119	0.8053	0.7411	0.0068	0.5590	0.7745	0.1544	0.7730	0.6432	0.7665	0.6144	0.0788	0.5308	0.5146	0.8012	0.7651	0.7687	0.7353	0.7305	0.6629	0.0068
0.1171	10.0	2000	0.1631	0.5504	0.6659	0.8147	nan	0.6300	0.8701	0.1681	0.8613	0.7188	0.8489	0.8576	0.0908	0.6585	0.6139	0.8979	0.8608	0.8580	0.8264	0.8141	0.7360	0.0096	0.5640	0.7730	0.1669	0.7699	0.6460	0.7657	0.6125	0.0854	0.5349	0.5162	0.8019	0.7652	0.7697	0.7366	0.7317	0.6573	0.0096

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Library: transformers
Type: segformer
License: other
Created: Apr 13, 2024
Updated: Apr 14, 2024