segformer-b2-human-parse-24

This model is a fine-tuned version of mattmdjaga/segformer_b2_clothes on the human_parsing_29_mix dataset. It achieves the following results on the evaluation set:

• Loss: 0.0818
• Mean Iou: 0.6023
• Mean Accuracy: 0.6321
• Overall Accuracy: 0.9780
• Accuracy Background: 0.9969
• Accuracy Hat: nan
• Accuracy Hair: 0.9646
• Accuracy Glove: 0.0
• Accuracy Glasses: 0.0
• Accuracy Upper Only Torso Region: 0.9747
• Accuracy Dresses Only Torso Region: 0.4939
• Accuracy Coat Only Torso Region: 0.0039
• Accuracy Socks: 0.0
• Accuracy Left Pants: 0.9604
• Accuracy Right Patns: 0.9646
• Accuracy Skin Around Neck Region: 0.9585
• Accuracy Scarf: nan
• Accuracy Skirts: 0.8904
• Accuracy Face: 0.9796
• Accuracy Left Arm: 0.9703
• Accuracy Right Arm: 0.9700
• Accuracy Left Leg: 0.9267
• Accuracy Right Leg: 0.9297
• Accuracy Left Shoe: 0.0
• Accuracy Right Shoe: 0.0
• Accuracy Left Sleeve For Upper: 0.9462
• Accuracy Right Sleeve For Upper: 0.9517
• Accuracy Bag: 0.0234
• Iou Background: 0.9941
• Iou Hat: nan
• Iou Hair: 0.9268
• Iou Glove: 0.0
• Iou Glasses: 0.0
• Iou Upper Only Torso Region: 0.9351
• Iou Dresses Only Torso Region: 0.4059
• Iou Coat Only Torso Region: 0.0035
• Iou Socks: 0.0
• Iou Left Pants: 0.9232
• Iou Right Patns: 0.9217
• Iou Skin Around Neck Region: 0.9227
• Iou Scarf: nan
• Iou Skirts: 0.7887
• Iou Face: 0.9582
• Iou Left Arm: 0.9436
• Iou Right Arm: 0.9426
• Iou Left Leg: 0.8836
• Iou Right Leg: 0.8767
• Iou Left Shoe: 0.0
• Iou Right Shoe: 0.0
• Iou Left Sleeve For Upper: 0.9005
• Iou Right Sleeve For Upper: 0.9012
• Iou Bag: 0.0232

Model description

More information needed

"id2label": {
    "0": "background",
    "1": "hat",
    "2": "hair",
    "3": "glove",
    "4": "glasses",
    "5": "upper_only_torso_region",
    "6": "dresses_only_torso_region",
    "7": "coat_only_torso_region",
    "8": "socks",
    "9": "left_pants",
    "10": "right_patns",
    "11": "skin_around_neck_region",
    "12": "scarf",
    "13": "skirts",
    "14": "face",
    "15": "left_arm",
    "16": "right_arm",
    "17": "left_leg",
    "18": "right_leg",
    "19": "left_shoe",
    "20": "right_shoe",
    "21": "left_sleeve_for_upper",
    "22": "right_sleeve_for_upper",
    "23": "bag"
}

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: 16
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Hat	Accuracy Hair	Accuracy Glove	Accuracy Glasses	Accuracy Upper Only Torso Region	Accuracy Dresses Only Torso Region	Accuracy Coat Only Torso Region	Accuracy Socks	Accuracy Left Pants	Accuracy Right Patns	Accuracy Skin Around Neck Region	Accuracy Scarf	Accuracy Skirts	Accuracy Face	Accuracy Left Arm	Accuracy Right Arm	Accuracy Left Leg	Accuracy Right Leg	Accuracy Left Shoe	Accuracy Right Shoe	Accuracy Left Sleeve For Upper	Accuracy Right Sleeve For Upper	Accuracy Bag	Iou Background	Iou Hat	Iou Hair	Iou Glove	Iou Glasses	Iou Upper Only Torso Region	Iou Dresses Only Torso Region	Iou Coat Only Torso Region	Iou Socks	Iou Left Pants	Iou Right Patns	Iou Skin Around Neck Region	Iou Scarf	Iou Skirts	Iou Face	Iou Left Arm	Iou Right Arm	Iou Left Leg	Iou Right Leg	Iou Left Shoe	Iou Right Shoe	Iou Left Sleeve For Upper	Iou Right Sleeve For Upper	Iou Bag
0.0652	1.62	1000	0.0802	0.5857	0.6166	0.9737	0.9963	nan	0.9490	0.0	0.0	0.9801	0.4034	0.0	0.0	0.9487	0.9574	0.9272	nan	0.8783	0.9782	0.9628	0.9534	0.8874	0.9012	0.0	0.0	0.9227	0.9197	0.0	0.9926	nan	0.9117	0.0	0.0	0.9217	0.3541	0.0	0.0	0.9084	0.9073	0.8963	nan	0.7766	0.9455	0.9210	0.9191	0.8405	0.8496	0.0	0.0	0.8673	0.8728	0.0
0.061	3.23	2000	0.0843	0.5977	0.6335	0.9747	0.9967	nan	0.9580	0.0	0.0	0.9657	0.5733	0.1504	0.0	0.9591	0.9600	0.9497	nan	0.8169	0.9789	0.9667	0.9645	0.8906	0.9165	0.0	0.0	0.9444	0.9445	0.0003	0.9935	nan	0.9199	0.0	0.0	0.9273	0.4058	0.1206	0.0	0.9131	0.9082	0.9128	nan	0.7330	0.9527	0.9355	0.9343	0.8534	0.8651	0.0	0.0	0.8860	0.8879	0.0003
0.0653	4.85	3000	0.0823	0.6000	0.6295	0.9775	0.9967	nan	0.9621	0.0	0.0	0.9780	0.4991	0.0044	0.0	0.9587	0.9649	0.9562	nan	0.8842	0.9769	0.9692	0.9651	0.9198	0.9273	0.0	0.0	0.9422	0.9415	0.0037	0.9939	nan	0.9247	0.0	0.0	0.9341	0.4136	0.0042	0.0	0.9202	0.9193	0.9193	nan	0.7899	0.9563	0.9403	0.9388	0.8745	0.8741	0.0	0.0	0.8963	0.8970	0.0037
0.0402	6.46	4000	0.0818	0.6023	0.6321	0.9780	0.9969	nan	0.9646	0.0	0.0	0.9747	0.4939	0.0039	0.0	0.9604	0.9646	0.9585	nan	0.8904	0.9796	0.9703	0.9700	0.9267	0.9297	0.0	0.0	0.9462	0.9517	0.0234	0.9941	nan	0.9268	0.0	0.0	0.9351	0.4059	0.0035	0.0	0.9232	0.9217	0.9227	nan	0.7887	0.9582	0.9436	0.9426	0.8836	0.8767	0.0	0.0	0.9005	0.9012	0.0232

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1
• Datasets 2.15.0
• Tokenizers 0.15.0