ModelPrésentéExterne

Food Freshness Detector

A ResNet18 image classifier trained with FastAI that predicts the freshness of fruits and vegetables.

Sourcehuggingface.co/nathansekar/food-freshness-detector/tree/7488ca2d7204656f2f5b464e1a295871396a513d

TypeOtherVersionRevision 7488ca2BClasse

Source du modèle

Type: Other
Taille du fichier: 44,7 MB
Version: vRevision 7488ca2
Mis à jour: 22 oct. 2026
Téléchargements: 160
Source: Hugging Face

Description de la source

Food Freshness Detector

A ResNet18 image classifier trained with FastAI that predicts the freshness of fruits and vegetables.

Classes: Fresh · Slightly Spoiled · Rotten

Usage

import json
import torch
from fastai.vision.all import create_cnn_model, resnet18
from torchvision import transforms
from PIL import Image
from huggingface_hub import hf_hub_download

# Download weights
weights_path = hf_hub_download("nathansekar/food-freshness-detector", "model_weights.pth")
config_path  = hf_hub_download("nathansekar/food-freshness-detector", "config.json")
vocab_path   = hf_hub_download("nathansekar/food-freshness-detector", "vocab.json")

with open(config_path) as f:
    config = json.load(f)
with open(vocab_path) as f:
    vocab = json.load(f)

model = create_cnn_model(resnet18, config["n_classes"])
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

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

with torch.no_grad():
    probs = torch.softmax(model(tensor), dim=1)[0]

pred_idx = probs.argmax().item()
print(f"Prediction: {vocab[pred_idx]} ({probs[pred_idx]:.1%})")

Model Details


Architecture	ResNet18 (transfer learning)
Framework	FastAI 2.x / PyTorch
Input	RGB image → 224×224
Output	3-class softmax
Training data	Synthetic fruit/vegetable images

Files

File	Description
`model_weights.pth`	Trained ResNet18 state dict
`config.json`	Architecture config (`arch`, `n_classes`, `img_size`)
`vocab.json`	Class label list

Sources

1 source

Hugging FacePrincipalVersion 7488ca2d7204656f2f5b464e1a295871396a513d

huggingface.co/nathansekar/food-freshness-detector/tree/7488ca2d7204656f2f5b464e1a295871396a513d

Vérifié 22 oct.

Artefacts du modèle

1 artefact

model_weights.pth

pth · 44,7 MB · SHA-256 61334d5fa30b…6dea · Hugging Face

Télécharger

Extraits de sources

3 extraits

Readme5 aoûthuggingface.co/nathansekar/food-freshness-detector

--- language: en license: mit tags: - image-classification - food - freshness - fastai - pytorch - resnet library_name: fastai --- # Food Freshness Detector A ResNet18 image classifier trained with FastAI that predicts the freshness of fruits and vegetables. **Classes:** Fresh · Slightly Spoiled · Rotten ## Usage ```python import json import torch from fastai.vision.all import create_cnn_model, resnet18 from torchvision import transforms from PIL import Image from huggingface_hub import hf_hub_download # Download weights weights_path = hf_hub_download("nathansekar/food-freshness-detector", "model_weights.pth") config_path = hf_hub_download("nathansekar/food-freshness-detector", "config.json") vocab_path = hf_hub_download("nathansekar/food-freshness-detector", "vocab.json") with open(config_path) as f: config = json.load(f) with open(vocab_path) as f: vocab = json.load(f) model = create_cnn_model(resnet18, config["n_classes"]) model.load_state_dict(torch.load(weights_path, map_location="cpu")) model.eval() preprocess = 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("your_food_image.jpg").convert("RGB") tensor = preprocess(image).unsqueeze(0) with torch.no_grad(): probs = torch.softmax(model(tensor), dim=1)[0] pred_idx = probs.argmax().item() print(f"Prediction: {vocab[pred_idx]} ({probs[pred_idx]:.1%})") ``` ## Model Details | | | |---|---| | Architecture | ResNet18 (transfer learning) | | Framework | FastAI 2.x / PyTorch | | Input | RGB image → 224×224 | | Output | 3-class softmax | | Training data | Synthetic fruit/vegetable images | ## Files | File | Description | |---|---| | `model_weights.pth` | Trained ResNet18 state dict | | `config.json` | Architecture config (`arch`, `n_classes`, `img_size`) | | `vocab.json` | Class label list |

Note22 oct.huggingface.co/nathansekar/food-freshness-detector

Source context: 160 downloads · 0 likes · Pipeline image-classification · Library fastai · Repo nathansekar/food-freshness-detector

Note5 aoûthuggingface.co/nathansekar/food-freshness-detector

Source context: 3909 downloads · 0 likes · Pipeline image-classification · Library fastai · Repo nathansekar/food-freshness-detector