apple-fastvlm-vision-encoder-1024

Apple FastVLM Vision Encoder (1024)

ONNX vision encoders for FastVLM image preprocessing and embedding extraction, provided in three precision variants so you can trade off accuracy, speed, and file size for your deployment.

Model files

Usage

Use whichever variant best fits your accuracy/performance needs for image embedding generation in FastVLM-style CPU pipelines.

import onnxruntime as ort

# Swap in whichever variant you need
session = ort.InferenceSession("vision_encoder_fp32.onnx")
# feed in your preprocessed image tensor and run inference

Choosing a variant

• FP32 — use when accuracy matters most and memory/size isn't a constraint.
• FP16 — a middle ground: smaller and often faster than FP32 with minimal accuracy loss on supporting hardware.
• INT8 — use for the smallest footprint and fastest CPU inference, with some tradeoff in numerical precision.

Base model

Derived from apple/FastVLM-0.5B.