FMCG(Fast-Moving Consumer Goods) brands require insights into retail shelves to help them improve their sales. One such insight comes from determining how many products of their brands’ are present versus how many products of competing brands are present on a retail store shelf. This requires finding the total number of products present on every shelf in a retail store.
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Given a grocery store shelf image, detect all products present in the shelf image (detection only at product or no-product level)
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The assignment requires to implement a single shot object detector with only “one” anchor box per feature-map cell.
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Accuracy of at least 0.7 mAP on the test set.
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The dataset to be used for training/testing is the Grocery dataset. Link to the dataset: https://github.com/gulvarol/grocerydataset
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Please use the following link to download ShelfImages.tar.gz(contains train and test splits) and replace GroceryDataset_part1/ShelfImages with this. https://storage.googleapis.com/open_source_datasets/ShelfImages.tar.gz
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Training Colab Notebook -
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Prediction Code Notebook -
{
'map@0.5' : 0.895
'map@0.75': 0.804
'averagePrecision@IOU_0.5': 0.895
'averagePrecision@IOU_0.75': 0.804
'averageRecall@IOU_0.5':0.70
}
