| --- |
| license: apache-2.0 |
| tags: [image-classification, food, binary-classification, vit, mobile] |
| --- |
| # Binary Healthy/Unhealthy Food Classifier — ViT-B/16 |
|
|
| Frozen ViT-B/16 + linear head. Trained on [ethz/food101](https://huggingface.co/datasets/ethz/food101). |
|
|
| ## Test metrics |
| - accuracy: **0.8088** |
| - macro F1: **0.8033** |
| - ROC-AUC: **0.8854** |
|
|
| ## Files |
| | File | Format | Use | |
| |---|---|---| |
| | `best.pth` | PyTorch state dict | training / fine-tuning | |
| | `model.torchscript.pt` | TorchScript | server / LibTorch | |
| | `model_mobile.ptl` | TorchScript Lite | **iOS / Android** (PyTorch Mobile) | |
| | `model.onnx` | ONNX | Core ML, TFLite (via onnx2tf), ONNX Runtime Mobile | |
|
|
| ## Inference (Python) |
| ```python |
| import torch, torchvision.transforms as T |
| from PIL import Image |
| m = torch.jit.load("model.torchscript.pt").eval() |
| tf = T.Compose([T.Resize((224,224)), T.ToTensor(), |
| T.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])]) |
| img = tf(Image.open("food.jpg").convert("RGB")).unsqueeze(0) |
| probs = torch.softmax(m(img), dim=-1)[0] |
| print({"healthy": probs[0].item(), "unhealthy": probs[1].item()}) |
| ``` |
|
|
