GVM

--- language: en license: apache-2.0 pipeline_tag: image-classification tags: - computer-vision - image-classification - mobilenet-v2 - cifar100 - whirlwindai datasets: - cifar100 metrics: - accuracy --- <p align="center"> <img src="https://raw.githubusercontent.com/Platane/snk/output/github-contribution-grid-snake-dark.svg"> </p> <div align="center"> <img src="https://readme-typing-svg.demolab.com?font=Space+Grotesk&weight=700&size=28&duration=2600&pause=1200&color=22D3EE&center=true&vCenter=true&width=720&lines=GVM;General+Vision+Model;Seeing+Patterns.;Built+for+Speed." /> <br> <img src="https://img.shields.io/badge/Model-MobileNetV2-06B6D4?style=for-the-badge"> <img src="https://img.shields.io/badge/Parameters-14MB-0EA5E9?style=for-the-badge"> <img src="https://img.shields.io/badge/Dataset-CIFAR--100-14B8A6?style=for-the-badge"> <img src="https://img.shields.io/badge/Framework-PyTorch-0284C7?style=for-the-badge"> <br><br> <img src="https://capsule-render.vercel.app/api?type=blur&height=180&text=GVM&fontSize=56&animation=twinkling&fontColor=ffffff&color=0:0891B2,50:06B6D4,100:22D3EE"/> </div> --- <div align="center"> # Vision, Simplified. Small models can recognize more than their size suggests. GVM explores efficient computer vision using lightweight architectures, fast inference, and practical deployment. Designed to run almost anywhere. </div> --- # Classification Performance <div align="center"> | Epoch | Training Loss | Validation Accuracy | |:------:|:-------------:|:------------------:| | **1** | 3.36 | **41.75%** | | **2** | 2.78 | **47.14%** | | **3** | 2.64 | **47.40%** | </div> --- # Quick Start ```python import torch import torchvision.transforms as transforms import timm import requests import json from PIL import Image config = json.loads( requests.get( "https://huggingface.co/WhirlwindAI/GVM/resolve/main/config.json" ).text ) model = timm.create_model( "mobilenetv2_100", pretrained=False, num_classes=config["num_classes"] ) state = torch.hub.load_state_dict_from_url( "https://huggingface.co/WhirlwindAI/GVM/resolve/main/model.pth", map_location="cpu" ) model.load_state_dict(state) model.eval() transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225] ) ]) image = Image.open(...

Source context: 18 downloads · 1 likes · Pipeline image-classification · Repo WhirlwindAI/GVM