# Gender-Classifier-Mini by Prithiv Sakthi: Open-Weight Model Source: https://savrn.com/models/gender-classifier-mini Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Gender-Classifier-Mini (93M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [Gender-Classifier-Mini on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/gender-classifier-mini/gpus) ## Model Card By Prithiv Sakthi, published under apache-2.0, revision 4855356b698e. Gender-Classifier-Mini is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images based on gender using the SiglipForImageClassification architecture. ``` Accuracy: 0.9720 F1 Score: 0.9720 Classification Report: precision recall f1-score support Female 0.9660 0.9796 0.9727 2549 Male 0.9785 0.9641 0.9712 2451 accuracy 0.9720 5000 macro avg 0.9722 0.9718 0.9720 5000 weighted avg 0.9721 0.9720 0.9720 5000 ``` The model categorizes images into two classes: - Class 0:"Female " -Class 1:"Male " ### Run with Transformers ``` !pip install -q transformers torch pillow gradio ``` [Read the full model card (255 words)](https://savrn.com/models/gender-classifier-mini/card) ## Configuration Architecture SiglipForImageClassification Context length (tokens) 64 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 256,000 Stored precision float32 Model type siglip ## Identity and Version Repository prithivMLmods/Gender-Classifier-Mini Publisher Prithiv Sakthi Task Image classification Modality Image Library transformers Parameters 93M parameters Languages en Revision 4855356b698e88ba57996836bb35091f81932667 First published 2025-03-23 Last updated 2025-03-28 ## Files and Weights 22 files, 2.5 GB in total. The weights are 12 files totalling 2.5 GB in bin, pt, pth, safetensors. Weights12 files · 2.5 GB Configuration8 files · 7.1 KB Documentation1 file · 3.1 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | checkpoint-157/model.safetensors | Weights | 371.6 MB | 4b3c881ec1fe | | checkpoint-157/optimizer.pt | Weights | 686.6 MB | 7e42ca610964 | | checkpoint-157/rng_state.pth | Weights | 14.2 KB | 86d5603fb947 | | checkpoint-157/scheduler.pt | Weights | 1.1 KB | a4e52ea24361 | | checkpoint-157/training_args.bin | Weights | 5.3 KB | 60bfe4936cb8 | | checkpoint-314/model.safetensors | Weights | 371.6 MB | 672245c638f4 | | checkpoint-314/optimizer.pt | Weights | 686.6 MB | ca2c61bce314 | | checkpoint-314/rng_state.pth | Weights | 14.2 KB | 1d268ac255a6 | | checkpoint-314/scheduler.pt | Weights | 1.1 KB | 8b7714de4736 | | checkpoint-314/training_args.bin | Weights | 5.3 KB | 60bfe4936cb8 | | model.safetensors | Weights | 371.6 MB | 672245c638f4 | | training_args.bin | Weights | 5.3 KB | 60bfe4936cb8 | | checkpoint-157/config.json | Configuration | 1.1 KB | — | | checkpoint-157/preprocessor_config.json | Configuration | 394 B | — | | checkpoint-157/trainer_state.json | Configuration | 1.1 KB | — | | checkpoint-314/config.json | Configuration | 1.1 KB | — | | checkpoint-314/preprocessor_config.json | Configuration | 394 B | — | | checkpoint-314/trainer_state.json | Configuration | 1.4 KB | — | | config.json | Configuration | 1.1 KB | — | | preprocessor_config.json | Configuration | 394 B | — | | README.md | Documentation | 3.1 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 2.5 GB [Download from Prithiv Sakthi](https://huggingface.co/prithivMLmods/Gender-Classifier-Mini) Released by Prithiv Sakthi through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [google/siglip2-base-patch16-224](https://savrn.com/models/siglip2-base-patch16-224) - Trained on (disclosed) myvision/gender-classification ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.5 GB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Gender-Classifier-Mini ### How much GPU memory does Gender-Classifier-Mini need? About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (93M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Gender-Classifier-Mini on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Gender-Classifier-Mini commercially? Yes. Gender-Classifier-Mini is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is Gender-Classifier-Mini's context length? 64 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Image classification ### [siglip2-x256-explicit-content](https://savrn.com/models/siglip2-x256-explicit-content) [Prithiv Sakthi](https://savrn.com/model-publishers/prithivmlmods) SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 The model classifies each image into one of the following content categories: This model is intended for applications such as Open weights apache-2.0 93M parameters 64 tokens transformers [View model](https://savrn.com/models/siglip2-x256-explicit-content) Model · Image classification ### [swin_base_patch4_window12_384.ms_in22k_ft_in1k](https://savrn.com/models/swin-base-patch4-window12-384-ms-in22k-ft-in1k) [PyTorch Image Models](https://savrn.com/model-publishers/timm) A Swin Transformer image classification model. 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ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various… Open weights apache-2.0 89M parameters transformers [View model](https://savrn.com/models/convnextv2-base-22k-384) Model · Image classification ### [convnextv2_base.fcmae_ft_in22k_in1k](https://savrn.com/models/convnextv2-base-fcmae-ft-in22k-in1k) [PyTorch Image Models](https://savrn.com/model-publishers/timm) A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. 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