PE-Core-ANE

PE-Core ANE (Apple Neural Engine) Models

Perception Encoder (PE-Core) models converted to CoreML format optimized for Apple Neural Engine (ANE).

Models

Performance (M3 Mac)

Usage (Python)

import coremltools as ct
import numpy as np

# Load model
model = ct.models.MLModel("PE-Core-B-16-ANE.mlpackage")

# Prepare image (1, 3, 224, 224) normalized
image = np.random.randn(1, 3, 224, 224).astype(np.float32)

# Get embedding
output = model.predict({"image": image})
embedding = output["embedding"]  # (1, 768)

# Normalize for similarity search
embedding = embedding / np.linalg.norm(embedding)

Usage (Swift)

import CoreML

let model = try MLModel(contentsOf: modelURL)
let input = try MLDictionaryFeatureProvider(dictionary: ["image": pixelBuffer])
let output = try model.prediction(from: input)
let embedding = output.featureValue(for: "embedding")!.multiArrayValue!

Conversion Details

• Source: Meta's Perception Encoder via open_clip
• Format: CoreML mlpackage (FP16)
• Target: macOS 14+ (ANE optimized)
• Accuracy: >99.98% cosine similarity vs PyTorch

Credits

• Original models: Meta AI Perception Encoder
• Loaded via: open_clip
• Converted with: coremltools