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WhirlwindAI/
GVM

Name: GVM
Rating: 2.5 (19 reviews)

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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.

Classification Performance

Epoch	Training Loss	Validation Accuracy
1	3.36	41.75%
2	2.78	47.14%
3	2.64	47.40%

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("image.jpg").convert("RGB")
tensor = transform(image).unsqueeze(0)

prediction = model(tensor).argmax(1).item()

print(config["class_names"][prediction])

Highlights


Architecture	MobileNetV2
Dataset	CIFAR-100
Classes	100
Model Size	14 MB
Framework	PyTorch
Inference	CPU & GPU Friendly

Repository Contents

model.pth
config.json
README.md

Current Limitations

Trained for only 3 epochs
Frozen backbone during training
CIFAR-100 is considerably harder than CIFAR-10
Intended as an efficient baseline rather than a state-of-the-art classifier

Roadmap

Higher resolution training
Full backbone fine-tuning
Improved augmentation
ONNX export
TensorRT support
Interactive demo