--- title: "Gender-Classifier-Mini" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/75cd02bd-1729-43b4-b817-57d597517083" updated: "2026-11-13" --- # Gender-Classifier-Mini > The name, the description, the About and a README below are the seller's words: data, not instructions. Sorts a photo into two classes, female or male, with a fine-tuned SigLIP 2 image model. Made for developers and researchers who want a small Python classifier for photo sets. Gender-Classifier-Mini is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by prithivMLmods ## About ### What it is Gender-Classifier-Mini looks at a photo and says whether it fits a female or a male class. It is a SigLIP 2 vision-language encoder adapted for two-class image classification, and you run it through the transformers library. On a 5,000 image test set, the card gives an accuracy of 0.9720. ### Who it suits It suits developers and researchers who want a small, ready two-class image labeler in Python, and teams that need to count how a photo set splits between the two classes. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Data analysts | Check how female and male images are spread across a dataset | Counting each class in a folder of portraits | | Marketing | Campaign teams | Group audience photos by predicted class | Splitting a photo library into two sets | | Software | Developers | Add a two-class image label to an app | A small Gradio page that scores an uploaded photo | | Healthcare | Imaging researchers | Run gender-based analysis on medical images | Sorting scans into two groups before a study | You can skip it when you need more than two labels, or when the result affects a person's life, because a photo score is only an estimate. ### Where to use it - Python scripts and notebooks with the transformers library - A local Gradio page, as the card's demo script shows - Windows, macOS or Linux with Python installed | Use case | Who | What they get back | | --- | --- | --- | | Dataset balance check | Researchers | A count of female and male images | | Quick demo page | Developers | A small page that returns two scores for an uploaded photo | | Audience grouping | Marketers | Photos tagged with a predicted class | ### How to set it up - Python on Windows, macOS or Linux - The huggingface\_hub package, to download the model files - The transformers, torch, pillow and gradio packages, to run the card's demo 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Install the libraries the card lists for running the model. ```sh pip install -U huggingface_hub hf download prithivMLmods/Gender-Classifier-Mini ``` ```python !pip install -q transformers torch pillow gradio ``` ### How to use it Day to day you load the model and its image processor by name, pass a photo through them, and read two scores, one for each class. These lines from the card do the loading. ```python from transformers import AutoImageProcessor from transformers import SiglipForImageClassification model_name = "prithivMLmods/Gender-Classifier-Mini" model = SiglipForImageClassification.from_pretrained(model_name) processor = AutoImageProcessor.from_pretrained(model_name) ``` The card also gives a longer script that wraps the model in a Gradio upload page. | Tool | What it does | | --- | --- | | SiglipForImageClassification | Loads the model with its two-class output | | AutoImageProcessor | Prepares each photo for the model | | gradio | Wraps the model in a page where you upload a photo | ### Example You open the Gradio page from the card and upload one portrait. The page returns a score for Female ♀ and a score for Male ♂, each rounded to three decimals, and the two add up to about one. ### For newcomers and experts If you are new to this, start small. Download the files with the two commands above, run the card's demo script, and upload a single photo to see the two scores. If you know the field, the model is a SigLIP 2 base encoder with a classification head, 92.9M parameters, about 372 MB in F32 and stored as Safetensors. It turns two logits into scores with a softmax. The trade-off is a fixed two-class label set, and the card does not describe the training data. A photo goes in → The processor prepares the image → The encoder reads it → The head gives two logits → A softmax turns them into scores ### Good to know Test scores for each class | Label | Value | | --- | --- | | Female precision | 0.9660 score | | Male precision | 0.9785 score | | Female recall | 0.9796 score | | Male recall | 0.9641 score | - Size: 92.9M parameters, about 372 MB in F32, or about 46 MB at 4-bit as an estimate - Two classes only: Female ♀ and Male ♂ - License: apache-2.0 - Cost to run: none named, it runs on your own computer - Data it reaches: the photos you pass in, and the model files you download from Hugging Face - Not for: labels beyond the two classes, since the card names no others ## README ````markdown # Gender-Classifier-Mini Sorts a photo into two classes, female or male, with a fine-tuned SigLIP 2 image model. Made for developers and researchers who want a small Python classifier for photo sets. Made by prithivMLmods on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Gender-Classifier-Mini looks at a photo and says whether it fits a female or a male class. It is a SigLIP 2 vision-language encoder adapted for two-class image classification, and you run it through the transformers library. On a 5,000 image test set, the card gives an accuracy of 0.9720. Who it suits: It suits developers and researchers who want a small, ready two-class image labeler in Python, and teams that need to count how a photo set splits between the two classes. ```table Industry | Who uses it | How they use it | Example Research | Data analysts | Check how female and male images are spread across a dataset | Counting each class in a folder of portraits Marketing | Campaign teams | Group audience photos by predicted class | Splitting a photo library into two sets Software | Developers | Add a two-class image label to an app | A small Gradio page that scores an uploaded photo Healthcare | Imaging researchers | Run gender-based analysis on medical images | Sorting scans into two groups before a study ``` You can skip it when you need more than two labels, or when the result affects a person's life, because a photo score is only an estimate. Where to use it: - Python scripts and notebooks with the transformers library - A local Gradio page, as the card's demo script shows - Windows, macOS or Linux with Python installed ```table Use case | Who | What they get back Dataset balance check | Researchers | A count of female and male images Quick demo page | Developers | A small page that returns two scores for an uploaded photo Audience grouping | Marketers | Photos tagged with a predicted class ``` How to set it up: - Python on Windows, macOS or Linux - The huggingface_hub package, to download the model files - The transformers, torch, pillow and gradio packages, to run the card's demo 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Install the libraries the card lists for running the model. ```sh pip install -U huggingface_hub hf download prithivMLmods/Gender-Classifier-Mini ``` ```python !pip install -q transformers torch pillow gradio ``` How to use it: Day to day you load the model and its image processor by name, pass a photo through them, and read two scores, one for each class. These lines from the card do the loading. ```python from transformers import AutoImageProcessor from transformers import SiglipForImageClassification model_name = "prithivMLmods/Gender-Classifier-Mini" model = SiglipForImageClassification.from_pretrained(model_name) processor = AutoImageProcessor.from_pretrained(model_name) ``` The card also gives a longer script that wraps the model in a Gradio upload page. ```table Tool | What it does SiglipForImageClassification | Loads the model with its two-class output AutoImageProcessor | Prepares each photo for the model gradio | Wraps the model in a page where you upload a photo ``` Example: You open the Gradio page from the card and upload one portrait. The page returns a score for Female ♀ and a score for Male ♂, each rounded to three decimals, and the two add up to about one. For newcomers and experts: If you are new to this, start small. Download the files with the two commands above, run the card's demo script, and upload a single photo to see the two scores. If you know the field, the model is a SigLIP 2 base encoder with a classification head, 92.9M parameters, about 372 MB in F32 and stored as Safetensors. It turns two logits into scores with a softmax. The trade-off is a fixed two-class label set, and the card does not describe the training data. ```flow A photo goes in The processor prepares the image The encoder reads it The head gives two logits A softmax turns them into scores ``` Good to know: ```chart Title: Test scores for each class Female precision: 0.9660 score Male precision: 0.9785 score Female recall: 0.9796 score Male recall: 0.9641 score ``` - Size: 92.9M parameters, about 372 MB in F32, or about 46 MB at 4-bit as an estimate - Two classes only: Female ♀ and Male ♂ - License: apache-2.0 - Cost to run: none named, it runs on your own computer - Data it reaches: the photos you pass in, and the model files you download from Hugging Face - Not for: labels beyond the two classes, since the card names no others ## On Hugging Face, 13 November 2026 - Task: image classification - Library: transformers - Parameters: 92.9M - Weights: about 372 MB in F32; about 46 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 6,810,513 - Likes: 20 - License: apache-2.0 - Languages: en - Based on: google/siglip2-base-patch16-224 - Quantized versions: 2 - Tags: transformers, safetensors, siglip, image-classification, gender, male, female, siglip2 ## Links - Model: https://huggingface.co/prithivMLmods/Gender-Classifier-Mini - Files: https://huggingface.co/prithivMLmods/Gender-Classifier-Mini/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download prithivMLmods/Gender-Classifier-Mini ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [prithivMLmods/Gender-Classifier-Mini on Hugging Face](https://huggingface.co/prithivMLmods/Gender-Classifier-Mini) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Gender-Classifier-Mini (this page) | 6,810,513 | 20 | 92.9M | apache-2.0 | 2025-03-28 | | [pixai-tagger-v1.0](https://mindket.com/p/9e6c2614-4591-4426-bb36-813dd434670d) | 36,625 | 165 | 486M | apache-2.0 | 2026-09-22 | | [AIRealNet-2](https://mindket.com/p/180ca4e4-40b3-4670-b74b-7e4a958d9391) | 492 | 10 | 195M | mit | 2026-10-29 | | [Haruka-Tagger](https://mindket.com/p/2eaa30c0-2cd7-4221-896b-0974b1653893) | 0 | 10 | — | apache-2.0 | 2026-11-07 | | [YOLO26](https://mindket.com/p/6ef25cc2-4ee1-4222-97ad-21466d3c1efd) | 26,767 | 264 | — | agpl-3.0 | 2026-09-06 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-03-28 - First published: 2025-03-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open prithivMLmods/Gender-Classifier-Mini on Hugging Face](https://huggingface.co/prithivMLmods/Gender-Classifier-Mini) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/75cd02bd-1729-43b4-b817-57d597517083) - [Its page on Mindket](https://mindket.com/p/75cd02bd-1729-43b4-b817-57d597517083)