yolo26n-indian-fmcg-detection

YOLO26n — Indian FMCG Product Detection

Fine-tuned YOLO26n on a merged dataset of three Indian grocery sources from Roboflow Universe. Part of the Kirana Detective project — an AI system for small Indian grocery stores to visually count and reconcile shelf/counter inventory from photos.

Performance

Training ran for 100 epochs (60 initial + 40 resumed after restart) on an NVIDIA A10G via Modal.

Training Datasets

All three datasets were downloaded in YOLOv8 format, class IDs remapped to a unified list, and merged before training. The full unified class list (1,831 entries) is in class_names.json.

Files

How to Use

ONNX Runtime (CPU / any platform)

import json, numpy as np, onnxruntime as ort
from PIL import Image

session     = ort.InferenceSession("best.onnx", providers=["CPUExecutionProvider"])
class_names = json.load(open("class_names.json"))

def preprocess(path, size=640):
    img = Image.open(path).convert("RGB").resize((size, size))
    return (np.array(img, dtype=np.float32) / 255.0).transpose(2, 0, 1)[None]

input_name = session.get_inputs()[0].name
outputs    = session.run(None, {input_name: preprocess("shelf.jpg")})
# outputs[0]: (1, 300, 6) — [x1, y1, x2, y2, confidence, class_id]

Ultralytics (PyTorch)

from ultralytics import YOLO

model   = YOLO("best.pt")
results = model.predict("shelf.jpg", imgsz=640, conf=0.25)
results[0].show()

Training Details

Citation

@misc{kirana-detective-yolo-2026,
  title  = {Kirana Detective: YOLO26n Indian FMCG Product Detector},
  author = {Syed Naazim Hussain},
  year   = {2026},
  url    = {https://huggingface.co/naazimsnh02/yolo26n-indian-fmcg-detection}
}