Giới thiệu
User nhập 172cm / 70kg, AI phải suggest: M regular hoặc L oversize. Size recommendation giảm tỷ lệ đổi trả (quan trọng cho POD vì design custom không thể đổi). Bài này xây dựng ML model cho size prediction.
1. Input Options
Option 1 — Basic (required):
├── height (cm)
└── weight (kg)
Option 2 — Enhanced:
├── height, weight
├── chest (cm)
├── waist (cm)
└── shoulder (cm)
Option 3 — Photo-based:
├── Upload ảnh người thật
└── AI estimate body measurements
(→ feed vào model Option 2)
2. Size Chart Data Model
SIZE_CHARTS = {
"US_STANDARD": {
"XS": {"chest": (81, 86), "waist": (66, 71), "weight": (45, 55)},
"S": {"chest": (86, 91), "waist": (71, 76), "weight": (55, 65)},
"M": {"chest": (91, 97), "waist": (76, 81), "weight": (65, 75)},
"L": {"chest": (97, 102), "waist": (81, 86), "weight": (75, 85)},
"XL": {"chest": (102, 107), "waist": (86, 91), "weight": (85, 95)},
"2XL":{"chest": (107, 112), "waist": (91, 97), "weight": (95, 110)},
},
"OVERSIZE": {
"M": {"chest": (97, 107), "waist": (81, 91), "weight": (60, 75)},
"L": {"chest": (107, 117), "waist": (86, 97), "weight": (70, 85)},
"XL": {"chest": (117, 127), "waist": (91, 102), "weight": (80, 100)},
},
}
3. ML Model cho Size Prediction
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
import numpy as np
class SizeRecommendationModel:
"""ML model dự đoán size áo"""
def __init__(self):
self.model = GradientBoostingClassifier(
n_estimators=200,
max_depth=5,
learning_rate=0.1,
random_state=42,
)
self.scaler = StandardScaler()
def train(self, X: np.ndarray, y: np.ndarray):
"""
X: [height, weight, chest?, waist?, shoulder?, bmi]
y: size label (XS, S, M, L, XL, 2XL)
"""
# Feature engineering
X_features = self._engineer_features(X)
X_scaled = self.scaler.fit_transform(X_features)
# Train
self.model.fit(X_scaled, y)
# Evaluate
scores = cross_val_score(
self.model, X_scaled, y, cv=5, scoring="accuracy"
)
print(f"CV Accuracy: {scores.mean():.3f} ± {scores.std():.3f}")
def predict(
self, height: float, weight: float,
chest: float | None = None,
waist: float | None = None,
shoulder: float | None = None,
) -> SizeRecommendation:
# Build feature vector
features = self._build_features(
height, weight, chest, waist, shoulder
)
features_scaled = self.scaler.transform([features])
# Get probabilities for each size
probs = self.model.predict_proba(features_scaled)[0]
classes = self.model.classes_
size_probs = dict(zip(classes, probs))
# Recommend: best regular + best oversize
regular_sizes = {k: v for k, v in size_probs.items()
if "oversize" not in k.lower()}
oversize_sizes = self._map_to_oversize(size_probs)
best_regular = max(regular_sizes, key=regular_sizes.get)
best_oversize = max(oversize_sizes, key=oversize_sizes.get)
return SizeRecommendation(
primary=best_regular,
primary_confidence=regular_sizes[best_regular],
oversize=best_oversize,
oversize_confidence=oversize_sizes[best_oversize],
all_probabilities=size_probs,
)
def _engineer_features(self, X: np.ndarray) -> np.ndarray:
"""Tạo thêm features từ raw measurements"""
height = X[:, 0]
weight = X[:, 1]
bmi = weight / (height / 100) ** 2
height_weight_ratio = height / weight
features = np.column_stack([
X, bmi, height_weight_ratio
])
return features
def _build_features(
self, height, weight, chest, waist, shoulder
) -> list:
bmi = weight / (height / 100) ** 2
hw_ratio = height / weight
# Handle missing optional measurements
chest = chest or self._estimate_chest(height, weight)
waist = waist or self._estimate_waist(height, weight)
shoulder = shoulder or self._estimate_shoulder(height, weight)
return [
height, weight, chest, waist, shoulder,
bmi, hw_ratio
]
def _estimate_chest(self, height: float, weight: float) -> float:
"""Ước tính vòng ngực từ height/weight"""
bmi = weight / (height / 100) ** 2
return 70 + bmi * 1.2 # Rough estimation
def _estimate_waist(self, height: float, weight: float) -> float:
bmi = weight / (height / 100) ** 2
return 55 + bmi * 1.5
def _estimate_shoulder(self, height: float, weight: float) -> float:
return height * 0.255 # ~25.5% of height
4. Multi-Fit Recommendation
@dataclass
class SizeRecommendation:
primary: str # "M"
primary_confidence: float # 0.85
oversize: str # "L"
oversize_confidence: float # 0.72
all_probabilities: dict # {"S": 0.05, "M": 0.85, ...}
def to_display(self) -> dict:
"""Format cho frontend display"""
return {
"recommendations": [
{
"size": self.primary,
"fit": "Regular fit",
"confidence": f"{self.primary_confidence:.0%}",
"description": "Vừa vặn theo body",
},
{
"size": self.oversize,
"fit": "Oversize fit",
"confidence": f"{self.oversize_confidence:.0%}",
"description": "Form rộng, thoải mái",
},
],
"size_chart": self.all_probabilities,
}
5. Feedback Loop
class SizeFeedbackLoop:
"""Học từ return/exchange data để cải thiện model"""
async def record_feedback(
self,
user_id: str,
ordered_size: str,
fit_feedback: str, # "too_small", "perfect", "too_large"
exchanged_to: str | None,
):
await self.feedback_store.insert({
"user_id": user_id,
"ordered_size": ordered_size,
"fit_feedback": fit_feedback,
"exchanged_to": exchanged_to,
"user_measurements": await self.get_measurements(user_id),
})
async def retrain_model(self):
"""Retrain model khi đủ feedback data"""
feedback_data = await self.feedback_store.get_all()
# Convert feedback thành training labels
# "perfect" → ordered_size là đúng
# "too_small" → exchanged_to là đúng
# "too_large" → exchanged_to là đúng
X, y = self._prepare_training_data(feedback_data)
self.model.train(X, y)
Tổng kết
AI Size Recommendation:
- Multi-input — height/weight cơ bản, measurements chi tiết, hoặc ảnh
- ML model — GradientBoosting với feature engineering
- Multi-fit — recommend cả regular và oversize
- Estimation — ước tính measurements thiếu từ height/weight
- Feedback loop — retrain từ return/exchange data
Bài tiếp theo bắt đầu Phần 5: Virtual Try-On — body estimation và 3D avatar.