pirocheto/schp-pascal-7
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SCHP — Self-Correction Human Parsing (Pascal Person Part, 7 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 Pascal Person Part dataset and packaged for the 🤗 Transformers AutoModel API.
Original repository: PeikeLi/Self-Correction-Human-Parsing
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
Usage — PyTorch
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:
id2label = model.config.id2label
print(id2label[1]) # → "Head"Usage — ONNX Runtime
Optimized ONNX files are available in the onnx/ folder of this repo:
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):
INT8 static quantization achieves 99.77% pixel-level agreement with the FP32 model.