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faster_rcnn_resnet50_coco

Use Case and High-Level Description

Faster R-CNN ResNet-50 model. Used for object detection. For details, see the paper.

Specification

Metric

Value

Type

Object detection

GFlops

57.203

MParams

29.162

Source framework

TensorFlow*

Accuracy

Metric

Value

coco_precision

31.09%

Input

Original Model

Image, name: image_tensor, shape: 1, 600, 1024, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: RGB.

Converted Model

  1. Image, name: image_tensor, shape: 1, 3, 600, 1024, format: B, C, H, W, where:

    • B - batch size

    • C - number of channels

    • H - image height

    • W - image width

      Expected color order: BGR.

  2. Information of input image size, name: image_info, shape: 1, 3, format: B, C, where:

    • B - batch size

    • C - vector of 3 values in format H, W, S, where H is an image height, W is an image width, S is an image scale factor (usually 1).

Output

Original Model

  1. Classifier, name: detection_classes. Contains predicted bounding boxes classes in a range [1, 91]. The model was trained on Common Objects in Context (COCO) dataset version with 91 categories of objects, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file

  2. Probability, name: detection_scores. Contains probability of detected bounding boxes.

  3. Detection box, name: detection_boxes. Contains detection boxes coordinates in format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates top left corner, (x_max, y_max) are coordinates right bottom corner. Coordinates are rescaled to input image size.

  4. Detections number, name: num_detections. Contains the number of predicted detection boxes.

Converted Model

The array of summary detection information, name: reshape_do_2d, shape: 1, 1, 100, 7 in the format 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 batch

  • label - predicted class ID in range [1, 91], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file

  • conf - confidence for the predicted class

  • (x_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])

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
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