https://github.com/sayakpaul/grocery-product-detection
This repository builds a product detection model to recognize products from grocery shelf images.
https://github.com/sayakpaul/grocery-product-detection
detection product-recognition tfod-api vision
Last synced: 6 months ago
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This repository builds a product detection model to recognize products from grocery shelf images.
- Host: GitHub
- URL: https://github.com/sayakpaul/grocery-product-detection
- Owner: sayakpaul
- License: apache-2.0
- Created: 2021-01-16T04:24:38.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-04-06T07:10:19.000Z (over 4 years ago)
- Last Synced: 2025-04-28T16:01:58.245Z (6 months ago)
- Topics: detection, product-recognition, tfod-api, vision
- Language: Jupyter Notebook
- Homepage:
- Size: 35.6 MB
- Stars: 93
- Watchers: 5
- Forks: 22
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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)) -
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.
## 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}
}
```