Grocery-Product-Detection

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Grocery-Product-Detection

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This repository builds a product detection model to recognize products from grocery shelf images.

Grocery-Product-Detection 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# Grocery-Product-Detection
This repository builds a product detection model to recognize products from grocery shelf images. The dataset comes from [here](https://github.com/gulvarol/grocerydataset). Everything from data preparation to model training is done using [Colab Notebooks](https://colab.research.google.com/) so that no setup is required locally. All the relevant commentaries have been included inside the Colab Notebooks.

## Repository organization

```
├── Colabs
│   ├── GroceryDataset_EDA_Prep.ipynb: EDA and data preparation notebook. 
│   ├── GroceryDataset_Evaluation.ipynb: Runs evaluation on the test images with the trained model.
│   ├── GroceryDataset_Inference.ipynb: Performs inference with the trained model.
│   └── GroceryDataset_Model_Training.ipynb: Trains an SSD MobileDet model using TFOD API.
├── Deliverables
│   ├── image2products.json: Contains test image names as keys and the number of products contained in each image as values.
│   └── metrics.json: mAP, precision and recall computed on test set.
├── Misc Files
│   ├── confusion_matrix.csv: Confusion matrix computed on the test set using the trained model.
│   ├── generate_tfrecord.py: Generates TFRecords from the provided dataset. 
│   └── ssdlite_mobiledet_dsp_320x320_products_sync_4x4.config: Configuration file needed by the TFOD API. 
└── README.md
```

## Results

Following snaps taken from TensorBoard after loading the evaluation logs (logs are available [here](https://github.com/sayakpaul/Grocery-Product-Detection/releases/download/v0.1.0/product-detection.zip)) -

![](https://i.ibb.co/7ttLyBt/Screenshot-2021-01-16-at-8-42-58-PM.png)

![](https://i.ibb.co/GMLTnwQ/Screenshot-2021-01-16-at-8-43-26-PM.png)

As we can see with 10k training steps the metrics keep on shining. I believe with more sophisticated hyperparameter tuning and a longer training schedule performance can further be improved.

![](https://i.ibb.co/xSxXSRF/Screenshot-2021-01-16-at-8-44-32-PM.png)

![](https://i.ibb.co/6gTxm6V/Screenshot-2021-01-16-at-8-46-51-PM.png)

## Notes
* The provided dataset is converted to TFRecords for easy compatibility with the TFOD API. Further notes are available inside the `Colabs/GroceryDataset_EDA_Prep.ipynb` notebook.
* Augmentation used
	* Horizontal flips
	* Random crops
* Detection network used: SSD MobileDet.
* Training hyperparameters are available inside `Misc\ Files/ssdlite_mobiledet_dsp_320x320_products_sync_4x4.config` file. 
* Precision and recall reported in `Deliverables/metrics.json` are mean values computed over the `precision_@0.5IOU` and `recall_@0.5IOU` columns of `Misc\ Files/confusion_matrix.csv`.
* A **threshold of 0.6** was used in order to obtain the number of products per test image.

## Trained model files

Find them [here](https://github.com/sayakpaul/Grocery-Product-Detection/releases/download/v0.1.0/product-detection.zip). If you are looking for the checkpoints, the latest ones are prefixed with `model.ckpt-10000`. There's also a frozen inference graph.

## Dataset citation
```
@article{varol16a,
      TITLE = {{Toward Retail Product Recognition on Grocery Shelves}},
      AUTHOR = {Varol, G{"u}l and Kuzu, Ridvan S.},
      JOURNAL = {ICIVC},
      YEAR = {2014}
}
```

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