mivolo v2 EU AI Act Compliance Profile

Model Card for MiVOLO V2 model

We introduce state-of-the-art multi-input transformer for age and gender estimation.

This model was trained on proprietary and open-source datasets.

MiVOLO V1 (224x224) architecture:

Inference Requirements and Model Introduction

• Resolution: Width and height of face/body crops must be 384px
• Precision: FP32 / FP16
• mivolo library

pip install git+https://github.com/WildChlamydia/MiVOLO.git

• transformers==4.51.0
• accelerate==1.8.1

Quick start

from transformers import AutoModelForImageClassification, AutoConfig, AutoImageProcessor
import torch
import cv2
import numpy as np
import requests

# load model and image processor
config = AutoConfig.from_pretrained(
    "iitolstykh/mivolo_v2", trust_remote_code=True
)
mivolo_model = AutoModelForImageClassification.from_pretrained(
    "iitolstykh/mivolo_v2", trust_remote_code=True, torch_dtype=torch.float16
)
image_processor = AutoImageProcessor.from_pretrained(
    "iitolstykh/mivolo_v2", trust_remote_code=True
)

# download test image
resp = requests.get('https://variety.com/wp-content/uploads/2023/04/MCDNOHA_SP001.jpg')
arr = np.asarray(bytearray(resp.content), dtype=np.uint8)
image = cv2.imdecode(arr, -1)

# face crops
x1, y1, x2, y2 = [625,  46, 686, 121]
faces_crops = [image[y1:y2, x1:x2]]  # may be [None] if bodies_crops is not None

# body crops
x1, y1, x

Your obligations as a mivolo v2 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Library