pirocheto/schp-atr-18

SCHP — Self-Correction Human Parsing (ATR, 18 classes)

SCHP (Self-Correction for Human Parsing) is a state-of-the-art human parsing model based on a ResNet-101 backbone. This checkpoint is trained on the ATR dataset and packaged for the 🤗 Transformers AutoModel API.

Original repository: PeikeLi/Self-Correction-Human-Parsing

Source image	Segmentation result

Use cases:

• 🎨 Outfit palette extraction — mask each clothing region (shirt, pants, dress…) then run color clustering to extract the dominant colors per garment
• 🏷️ Product tagging for e-commerce — automatically label uploaded photos with clothing categories before indexing in a catalog
• 👚 Virtual try-on pre-processing — generate clean garment masks (upper-clothes, skirt, dress…) as segmentation input to try-on models such as VITON or LaDI-VTON
• ✏️ Dataset annotation — accelerate labeling pipelines for fashion datasets by using predicted masks as initial annotations to correct manually
• ✂️ Clothing area cropping — crop tight bounding boxes around specific items (e.g. only the bag, only the shoes) for downstream classification or retrieval models

Dataset — ATR

ATR is a large single-person human parsing dataset with 17 000+ images focused on fashion AI.

• mIoU on ATR test: 82.29%
• 18 labels covering clothing items and body parts

Labels

ID	Label	ID	Label	ID	Label
0	Background	6	Pants	12	Left-leg
1	Hat	7	Dress	13	Right-leg
2	Hair	8	Belt	14	Left-arm
3	Sunglasses	9	Left-shoe	15	Right-arm
4	Upper-clothes	10	Right-shoe	16	Bag
5	Skirt	11	Face	17	Scarf

Usage — PyTorch

from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import torch

model = AutoModelForSemanticSegmentation.from_pretrained("pirocheto/schp-atr-18", trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained("pirocheto/schp-atr-18", trust_remote_code=True)

image = Image.open("photo.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

# outputs.logits       — (1, 18, 512, 512) raw logits
# outputs.parsing_logits — (1, 18, 512, 512) refined parsing logits
# outputs.edge_logits    — (1,  1, 512, 512) edge prediction logits
seg_map = outputs.logits.argmax(dim=1).squeeze().numpy()  # (H, W), values in [0, 17]

Each pixel in seg_map is a label ID. To map IDs back to names:

id2label = model.config.id2label
print(id2label[4])  # → "Upper-clothes"

Usage — ONNX Runtime

Optimized ONNX files are available in the onnx/ folder of this repo:

File	Size	Notes
onnx/schp-atr-18.onnx + .onnx.data	~257 MB	FP32, dynamic batch
onnx/schp-atr-18-int8-static.onnx	~66 MB	INT8 static, 99.94% pixel agreement

import onnxruntime as ort
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import AutoImageProcessor
from PIL import Image

model_path = hf_hub_download("pirocheto/schp-atr-18", "onnx/schp-atr-18-int8-static.onnx")
processor  = AutoImageProcessor.from_pretrained("pirocheto/schp-atr-18", trust_remote_code=True)

sess_opts = ort.SessionOptions()
sess_opts.intra_op_num_threads = 8
sess = ort.InferenceSession(model_path, sess_opts, providers=["CPUExecutionProvider"])

image  = Image.open("photo.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="np")
logits = sess.run(["logits"], {"pixel_values": inputs["pixel_values"]})[0]
seg_map = logits.argmax(axis=1).squeeze()  # (H, W)

Performance

Benchmarked on CPU (16-core, 8 ORT threads, intra_op_num_threads=8):

Backend	Latency	Speedup	Size
PyTorch FP32	~430 ms	1×	256 MB
ONNX FP32	~293 ms	1.5×	257 MB
ONNX INT8 static	~229 ms	1.9×	66 MB

INT8 static quantization achieves 99.94% pixel-level agreement with the FP32 model.

Model Details

Property	Value
Architecture	ResNet-101 + SCHP self-correction
Input size	512 × 512
Output	3 heads: logits, parsing_logits, edge_logits
Training dataset	ATR
Number of classes	18
Framework	PyTorch / Transformers

Citation

@article{li2020self,
  title={Self-Correction for Human Parsing},
  author={Li, Peike and Xu, Yunqiu and Wei, Yunchao and Yang, Yi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020},
  doi={10.1109/TPAMI.2020.3048039}
}