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Fine-tuned EfficientNetV2-S model for recognising Nigerian foods in photos. Given a plate image it returns every food item visible along with a confidence score.
Model source
Source description
Fine-tuned EfficientNetV2-S model for recognising Nigerian foods in photos. Given a plate image it returns every food item visible along with a confidence score.
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1 sourceVerified Sep 16
Model artifacts
1 artifactSource excerpts
2 excerpts
import torch
import torch.nn as nn
from torchvision import models, transforms
from torchvision.models import EfficientNet_V2_S_Weights
from huggingface_hub import hf_hub_download
from PIL import Image
import json
# Download model files
model_path = hf_hub_download("yusasif/Nigerian-food-Lens", "best_model.pth")
vocab_path = hf_hub_download("yusasif/Nigerian-food-Lens", "label_vocab.json")
# Load vocab
with open(vocab_path) as f:
vocab = json.load(f)
labels = vocab["labels"]
# Load model
checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
m = models.efficientnet_v2_s(weights=None)
m.classifier = nn.Sequential(
nn.Dropout(p=0.3, inplace=True),
nn.Linear(m.classifier[1].in_features, len(labels)),
)
m.load_state_dict(checkpoint["model_state_dict"])
m.eval()
# Preprocess image
transform = transforms.Compose([
transforms.Resize((384, 384)),
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 = transform(image).unsqueeze(0)
# Predict
with torch.no_grad():
probs = torch.sigmoid(m(tensor)).squeeze(0).numpy()
# Top 3 results
top3 = sorted(enumerate(probs), key=lambda x: x[1], reverse=True)[:3]
for idx, score in top3:
print(f"{labels[idx]}: {score*100:.1f}%")
# Run the API server
docker compose up --build
# Send an image
curl -X POST http://localhost:8003/detect \
-F "[email protected]"
Response:
{
"detected": [
{"food": "Egusi soup", "confidence": 0.87},
{"food": "Eba", "confidence": 0.81},
{"food": "Stockfish", "confidence": 0.74}
]
}
Trained on the NutriMama Nigerian food dataset — a multi-label annotated collection of Yoruba, Igbo, and Hausa dishes covering swallows, soups, proteins, rice dishes, snacks and street food.
--- language: en tags: - image-classification - multi-label-classification - nigerian-food - efficientnet - food-recognition - nutrimama license: apache-2.0 --- # Nigerian Food Lens — Multi-Label Food Detector Fine-tuned **EfficientNetV2-S** model for recognising Nigerian foods in photos. Given a plate image it returns every food item visible along with a confidence score. ## Model Details - **Architecture**: EfficientNetV2-S (torchvision), fine-tuned classifier head - **Training**: Multi-label classification with weighted BCE loss - **Input**: RGB image, resized to 384×384 - **Output**: Sigmoid probabilities per class — all items above threshold are returned - **Threshold**: 0.3 (recommended) ## How to Use ### Python (direct) ```python import torch import torch.nn as nn from torchvision import models, transforms from torchvision.models import EfficientNet_V2_S_Weights from huggingface_hub import hf_hub_download from PIL import Image import json # Download model files model_path = hf_hub_download("yusasif/Nigerian-food-Lens", "best_model.pth") vocab_path = hf_hub_download("yusasif/Nigerian-food-Lens", "label_vocab.json") # Load vocab with open(vocab_path) as f: vocab = json.load(f) labels = vocab["labels"] # Load model checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) m = models.efficientnet_v2_s(weights=None) m.classifier = nn.Sequential( nn.Dropout(p=0.3, inplace=True), nn.Linear(m.classifier[1].in_features, len(labels)), ) m.load_state_dict(checkpoint["model_state_dict"]) m.eval() # Preprocess image transform = transforms.Compose([ transforms.Resize((384, 384)), 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 = transform(image).unsqueeze(0) # Predict with torch.no_grad(): probs = torch.sigmoid(m(tensor)).squeeze(0).numpy() # Top 3 results top3 = sorted(enumerate(probs), key=lambda x: x[1], reverse=True)[:3] for idx, score in top3: print(f"{labels[idx]}: {score*100:.1f}%") ``` ### REST API (Docker) ```bash # Run the API server docker compose up --build # Send an image curl -X POST http://localhost:8003/detect \ -F "[email protected]" ``` Response: ```json { "detected": [ {"food": "Egusi soup", "confidence": 0.87}, {"food": "Eba", "confidence": 0.81}, {"food": "Stockfish", "conf...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo yusasif/Nigerian-food-recognision