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 Pascal Person Part dataset and packaged for the 🤗 Transformers AutoModel API.

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

Source image	Segmentation result
demo	demo-pascal

Use cases:

🏃 Body part segmentation — segment coarse body regions (head, torso, arms, legs) for pose-aware applications
🎮 Avatar rigging — generate body part masks as a preprocessing step for AR/VR avatars
🏥 Medical / ergonomics — coarse body region detection for posture analysis or wearable device placement
📐 Body proportion estimation — measure relative areas of body segments in 2D images

Dataset — Pascal Person Part

Pascal Person Part is a single-person human parsing dataset with 3 000+ images focused on body part segmentation.

mIoU on Pascal Person Part validation: 71.46%
7 coarse labels covering body regions

Labels

ID	Label
0	Background
1	Head
2	Torso
3	Upper Arms
4	Lower Arms
5	Upper Legs
6	Lower Legs

Usage — PyTorch

python

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

model = AutoModelForSemanticSegmentation.from_pretrained("pirocheto/schp-pascal-7", trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained("pirocheto/schp-pascal-7", 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,  7, 512, 512) raw logits
# outputs.parsing_logits — (1,  7, 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, 6]

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

python

id2label = model.config.id2label
print(id2label[1])  # → "Head"

Usage — ONNX Runtime

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

File	Size	Notes
`onnx/schp-pascal-7.onnx` + `.onnx.data`	~257 MB	FP32, dynamic batch
`onnx/schp-pascal-7-int8-static.onnx`	~66 MB	INT8 static, 99.77% pixel agreement

python

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-pascal-7", "onnx/schp-pascal-7-int8-static.onnx")
processor  = AutoImageProcessor.from_pretrained("pirocheto/schp-pascal-7", 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	~424 ms	1×	255 MB
ONNX FP32	~296 ms	1.44×	256 MB
ONNX INT8 static	~218 ms	1.94×	66 MB

INT8 static quantization achieves 99.77% 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
num_labels	7
Dataset	Pascal Person Part
Original mIoU	71.46%