metadata

license: mit
task_categories:
  - image-classification
  - zero-shot-image-classification
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': rocher_chocolate
            '1': milka_chocolate
            '2': kinder_chocolate
            '3': toblerone_white
            '4': toblerone_black
            '5': lays_classic
            '6': lays_chill
            '7': pringles_original
            '8': pringles_paprika
            '9': nestle_water
            '10': sanpellegrino_water
            '11': redbull_energydrink
            '12': monster_energydrink
            '13': heinz_ketchup
            '14': heinz_mayo
            '15': barilla_pesto
            '16': barilla_pomodoro
            '17': barilla_lasagne
            '18': loreal_shampoo
            '19': dove_soap
            '20': sensodyne_toothpaste
            '21': colgate_toothpaste
            '22': sensodyne_mouthwash
            '23': nivea_rollon
            '24': rexona_spray
            '25': dove_spray
            '26': nivea_baby
            '27': johnson_baby
    - name: image_hash
      dtype: string
    - name: product_name
      dtype: string
    - name: sample_id
      dtype: string
  splits:
    - name: train
      num_bytes: 437784366.375
      num_examples: 10357
    - name: test
      num_bytes: 193054870
      num_examples: 4920
  download_size: 630652408
  dataset_size: 630839236.375

Micro Market Experience (MIMEX) Product Recognition Dataset

Dataset Description

The Micro Market Experience (MIMEX) Dataset is a comprehensive collection of product images tailored for retail and consumer product classification tasks. This dataset encompasses 15,277 images across 28 product categories, including chocolates, snacks, beverages, personal care items, and food products.

Dataset Features

Total Images: 15,277
Number of Classes: 28
Splits: Train (10,357) / Test (4,920)
Image Format: PNG, RGB
Resolution: Variable (original product images)

Product Categories

The dataset includes the following 28 product categories:

0: Rocher Chocolate
1: Milka Chocolate
2: Kinder Chocolate
3: Toblerone White
4: Toblerone Black
5: Lays Classic
6: Lays Chill
7: Pringles Original
8: Pringles Paprika
9: Nestle Water
10: Sanpellegrino Water
11: Redbull Energydrink
12: Monster Energydrink
13: Heinz Ketchup
14: Heinz Mayo
15: Barilla Pesto
16: Barilla Pomodoro
17: Barilla Lasagne
18: Loreal Shampoo
19: Dove Soap
20: Sensodyne Toothpaste
21: Colgate Toothpaste
22: Sensodyne Mouthwash
23: Nivea Rollon
24: Rexona Spray
25: Dove Spray
26: Nivea Baby
27: Johnson Baby

Dataset Structure

mimex-dataset/
├── train/           # Training images (10,357 samples)
├── test/            # Test images (4,920 samples)
└── metadata/        # Dataset information and mappings

Data Fields

image: PIL Image of the product
label: Integer class label (0-27)
image_hash: Unique 12-character hash identifier
product_name: Human-readable product name
sample_id: Original sample identifier from source dataset

How to Use

Loading the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("Anilot/MIMEX")

# Load specific split
train_dataset = load_dataset("Anilot/MIMEX", split="train")
test_dataset = load_dataset("Anilot/MIMEX", split="test")

print(f"Train samples: {len(train_dataset)}")
print(f"Test samples: {len(test_dataset)}")

Basic Usage Example

import matplotlib.pyplot as plt
from datasets import load_dataset

# Load dataset
dataset = load_dataset("Anilot/MIMEX", split="train")

# Show first few samples
fig, axes = plt.subplots(2, 3, figsize=(12, 8))
for i, ax in enumerate(axes.flat):
    sample = dataset[i]
    ax.imshow(sample['image'])
    ax.set_title(f"{sample['product_name']}\nLabel: {sample['label']}")
    ax.axis('off')
plt.tight_layout()
plt.show()

License

This dataset is released under the MIT License. Please cite appropriately if used in research.

Citation

If you use this dataset in your research, please cite:

Tur, Anil Osman, et al. "Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models." 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI). IEEE, 2024.

DOI: 10.1109/RTSI61910.2024.10761839

BibTeX

@inproceedings{tur2024exploring,
  title={Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models},
  author={Tur, Anil Osman and Conti, Alessandro and Beyan, Cigdem and Boscaini, Davide and Larcher, Roberto and Messelodi, Stefano and Poiesi, Fabio and Ricci, Elisa},
  booktitle={2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)},
  pages={97--102},
  year={2024},
  organization={IEEE}
}