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--- |
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library_name: keras |
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tags: |
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- ObjectDetection |
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- RetinaNet |
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- ResNet50 |
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- ObjectClassification |
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- Feature Pyramid Network |
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pipeline_tag: object-detection |
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--- |
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## Model description |
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Implementing RetinaNet: Focal Loss for Dense Object Detection. |
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This repo contains the model for the notebook [**Object Detection with RetinaNet**](https://keras.io/examples/vision/retinanet/) |
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Here the model is tasked with localizing the objects present in an image, and at the same time, classifying them into different categories. In this, RetinaNet has been implemented, a popular `single-stage detector`, which is accurate and runs fast. RetinaNet uses a `feature pyramid network` to efficiently detect objects at multiple scales and introduces a new loss, the `Focal loss function`, to alleviate the problem of the extreme foreground-background class imbalance. |
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Full credits go to [**Srihari Humbarwadi**](https://twitter.com/srihari_rh) |
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## References |
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* [RetinaNet Paper](https://arxiv.org/abs/1708.02002) |
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* [Feature Pyramid Network Paper](https://arxiv.org/abs/1612.03144) |
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## Training and evaluation data |
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The dataset used here is a [COCO2017 dataset](https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | momentum | nesterov | training_precision | |
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|----|-------------|-----|--------|--------|------------------| |
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|SGD|{'class_name': 'PiecewiseConstantDecay', 'config': {'boundaries': [125, 250, 500, 240000, 360000], 'values': [2.5e-06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e-05], 'name': None}}|0.0|0.8999999761581421|False|float32| |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
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<center> |
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Model Reproduced By <u><a href="https://github.com/robotjellyzone"><b>Kavya Bisht</b></a></u> |
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</center> |
