RetinaNet is the dense object detection model with ResNet50 backbone, originally trained on Keras*, then converted to TensorFlow* protobuf format. For details, see paper, repository.
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commit)
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keras_to_tensorflow.patch<tt>: `` git apply keras_to_tensorflow.patch ```Metric | Value |
---|---|
Type | Object detection |
GFlops | 238.9469 |
MParams | 64.9706 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
coco_precision | 33.15% |
Image, name: input_1
, shape: [1x1333x1333x3], format: [BxHxWxC], where:
Expected color order: BGR. Mean values: [103.939, 116.779, 123.68]
Image, name: input_1
, shape: [1x3x1333x1333], format: [BxCxHxW], where:
Expected color order: BGR.
filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3
. Contains predicted bounding boxes classes in a range [1, 80]. The model was trained on the Microsoft* COCO dataset version with 80 categories of objects.filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3
. Contains probability of detected bounding boxes.filtered_detections/map/TensorArrayStack/TensorArrayGatherV3
. Contains detection boxes coordinates in a format [y_min, x_min, y_max, x_max]
, where (x_min
, y_min
) are coordinates of the top left corner, (x_max
, y_max
) are coordinates of the right bottom corner. Coordinates are rescaled to input image size.The array of summary detection information, name - DetectionOutput
, shape - [1, 1, N, 7], where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
], where:
image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box corner (coordinates stored in normalized format, in range [0, 1])x_max
, y_max
) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in range [0, 1])The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.