---
language: en
license: apache-2.0
pipeline_tag: image-classification
tags:
- computer-vision
- image-classification
- mobilenet-v2
- cifar100
- whirlwindai
datasets:
- cifar100
metrics:
- accuracy
---
---
# 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
---
