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Xyphitos/
fridgeai-spoilage

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FridgeAI Spoilage Classifier (MobileNetV3-Small)

Binary classifier that detects whether a food item is fresh or spoiled from an image crop.

Model Details

Architecture: MobileNetV3-Small with custom classifier head
Training data: Fresh and Rotten Fruits Dataset (Sriram, Kaggle) — ~13,500 images, 6 fruit classes × 2 labels
Training: 15 epochs, Adam lr=1e-3, BCEWithLogitsLoss, WeightedRandomSampler
Accuracy: 100% on test set (2,698 images)
Input: 224×224 RGB image
Output: P(spoiled) ∈ [0, 1]

Usage

python

import requests

API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/fridgeai-spoilage"
headers = {"Authorization": "Bearer hf_..."}

with open("apple_crop.jpg", "rb") as f:
    response = requests.post(API_URL, headers=headers, data=f.read())

print(response.json())
# [{"label": "spoiled", "score": 0.87}, {"label": "fresh", "score": 0.13}]

Xyphitos/fridgeai-spoilage

FridgeAI Spoilage Classifier (MobileNetV3-Small)

Model Details

Usage

Xyphitos/
fridgeai-spoilage