--- arxiv: 2403.02302 --- # Model Card for MiVOLO V2 model

🤗 Space | 🌐 Github | 📜 MiVOLO Paper (2023) 📜 MiVOLO Paper (2024)

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: ![img](https://raw.githubusercontent.com/WildChlamydia/MiVOLO/refs/heads/main/images/MiVOLO.jpg) ## Inference Requirements and Model Introduction + Resolution: Width and height of face/body crops must be `384px` + Precision: FP32 / FP16 + `mivolo` library ```shell pip install git+https://github.com/WildChlamydia/MiVOLO.git ``` + transformers==4.51.0 + accelerate==1.8.1 ## Quick start ```python 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, x2, y2 = [534, 16, 790, 559] bodies_crops = [image[y1:y2, x1:x2]] # may be [None] if faces_crops is not None # prepare BGR inputs faces_input = image_processor(images=faces_crops)["pixel_values"] body_input = image_processor(images=bodies_crops)["pixel_values"] faces_input = faces_input.to(dtype=mivolo_model.dtype, device=mivolo_model.device) body_input = body_input.to(dtype=mivolo_model.dtype, device=mivolo_model.device) # inference output = mivolo_model(faces_input=faces_input, body_input=body_input) # print results age = output.age_output[0].item() print(f"age: {round(age, 2)}") id2label = config.gender_id2label gender = id2label[output.gender_class_idx[0].item()] gender_prob = output.gender_probs[0].item() print(f"gender: {gender} [{int(gender_prob * 100)}%]") ``` ### Model Metrics

Model	Test Dataset	Age Accuracy	Gender Accuracy
mivolov2_384x384 (fp16)	Adience	70.2	97.3

## Citation 🌟 If you find our work helpful, please consider citing our papers and leaving valuable stars ```bibtex @article{mivolo2023, Author = {Maksim Kuprashevich and Irina Tolstykh}, Title = {MiVOLO: Multi-input Transformer for Age and Gender Estimation}, Year = {2023}, Eprint = {arXiv:2307.04616}, } ``` ```bibtex @article{mivolo2024, Author = {Maksim Kuprashevich and Grigorii Alekseenko and Irina Tolstykh}, Title = {Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation}, Year = {2024}, Eprint = {arXiv:2403.02302}, } ``` ## License Please, see [here](https://github.com/WildChlamydia/MiVOLO/tree/main/license).

iitolstykh/mivolo_v2

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