Model description
Implementing RetinaNet: Focal Loss for Dense Object Detection.
This repo contains the model for the notebook Object Detection with RetinaNet
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.
Full credits go to Srihari Humbarwadi
References
Training and evaluation data
The dataset used here is a COCO2017 dataset
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | momentum | nesterov | training_precision |
|---|---|---|---|---|---|
| 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 |
Model Plot
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