//! Module defining object detection and image segmentation models available to download.
//!
//! See [https://github.com/onnx/models#object_detection](https://github.com/onnx/models#object_detection)
// Acronyms are specific ONNX model names and contains upper cases
#![allow(clippy::upper_case_acronyms)]
use crate::download::{vision::Vision, AvailableOnnxModel, ModelUrl};
/// Object Detection & Image Segmentation
///
/// > Object detection models detect the presence of multiple objects in an image and segment out areas of the
/// > image where the objects are detected. Semantic segmentation models partition an input image by labeling each pixel
/// > into a set of pre-defined categories.
///
/// Source: [https://github.com/onnx/models#object_detection](https://github.com/onnx/models#object_detection)
#[derive(Debug, Clone)]
pub enum ObjectDetectionImageSegmentation {
/// A real-time CNN for object detection that detects 20 different classes. A smaller version of the
/// more complex full YOLOv2 network.
///
/// Variant downloaded: ONNX Version 1.3 with Opset Version 8.
TinyYoloV2,
/// Single Stage Detector: real-time CNN for object detection that detects 80 different classes.
///
/// Variant downloaded: ONNX Version 1.5 with Opset Version 10.
Ssd,
/// A variant of MobileNet that uses the Single Shot Detector (SSD) model framework. The model detects 80
/// different object classes and locates up to 10 objects in an image.
///
/// Variant downloaded: ONNX Version 1.7.0 with Opset Version 10.
SSDMobileNetV1,
/// Increases efficiency from R-CNN by connecting a RPN with a CNN to create a single, unified network for
/// object detection that detects 80 different classes.
///
/// Variant downloaded: ONNX Version 1.5 with Opset Version 10.
FasterRcnn,
/// A real-time neural network for object instance segmentation that detects 80 different classes. Extends
/// Faster R-CNN as each of the 300 elected ROIs go through 3 parallel branches of the network: label
/// prediction, bounding box prediction and mask prediction.
///
/// Variant downloaded: ONNX Version 1.5 with Opset Version 10.
MaskRcnn,
/// A real-time dense detector network for object detection that addresses class imbalance through Focal Loss.
/// RetinaNet is able to match the speed of previous one-stage detectors and defines the state-of-the-art in
/// two-stage detectors (surpassing R-CNN).
///
/// Variant downloaded: ONNX Version 1.6.0 with Opset Version 9.
RetinaNet,
/// A CNN model for real-time object detection system that can detect over 9000 object categories. It uses a
/// single network evaluation, enabling it to be more than 1000x faster than R-CNN and 100x faster than
/// Faster R-CNN.
///
/// Variant downloaded: ONNX Version 1.3 with Opset Version 8.
YoloV2,
/// A CNN model for real-time object detection system that can detect over 9000 object categories. It uses
/// a single network evaluation, enabling it to be more than 1000x faster than R-CNN and 100x faster than
/// Faster R-CNN. This model is trained with COCO dataset and contains 80 classes.
///
/// Variant downloaded: ONNX Version 1.5 with Opset Version 9.
YoloV2Coco,
/// A deep CNN model for real-time object detection that detects 80 different classes. A little bigger than
/// YOLOv2 but still very fast. As accurate as SSD but 3 times faster.
///
/// Variant downloaded: ONNX Version 1.5 with Opset Version 10.
YoloV3,
/// A smaller version of YOLOv3 model.
///
/// Variant downloaded: ONNX Version 1.6 with Opset Version 11.
TinyYoloV3,
/// Optimizes the speed and accuracy of object detection. Two times faster than EfficientDet. It improves
/// YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the COCO 2017 dataset and
/// FPS of 41.7 on Tesla 100.
///
/// Variant downloaded: ONNX Version 1.6 with Opset Version 11.
YoloV4,
/// Deep CNN based pixel-wise semantic segmentation model with >80% mIOU (mean Intersection Over Union).
/// Trained on cityscapes dataset, which can be effectively implemented in self driving vehicle systems.
///
/// Variant downloaded: ONNX Version 1.2.2 with Opset Version 7.
Duc,
}
impl ModelUrl for ObjectDetectionImageSegmentation {
fn fetch_url(&self) -> &'static str {
match self {
ObjectDetectionImageSegmentation::TinyYoloV2 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8.onnx",
ObjectDetectionImageSegmentation::Ssd => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/ssd/model/ssd-10.onnx",
ObjectDetectionImageSegmentation::SSDMobileNetV1 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10.onnx",
ObjectDetectionImageSegmentation::FasterRcnn => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx",
ObjectDetectionImageSegmentation::MaskRcnn => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/mask-rcnn/model/MaskRCNN-10.onnx",
ObjectDetectionImageSegmentation::RetinaNet => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/retinanet/model/retinanet-9.onnx",
ObjectDetectionImageSegmentation::YoloV2 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov2/model/yolov2-voc-8.onnx",
ObjectDetectionImageSegmentation::YoloV2Coco => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov2-coco/model/yolov2-coco-9.onnx",
ObjectDetectionImageSegmentation::YoloV3 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov3/model/yolov3-10.onnx",
ObjectDetectionImageSegmentation::TinyYoloV3 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/tiny-yolov3/model/tiny-yolov3-11.onnx",
ObjectDetectionImageSegmentation::YoloV4 => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov4/model/yolov4.onnx",
ObjectDetectionImageSegmentation::Duc => "https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/duc/model/ResNet101-DUC-7.onnx",
}
}
}
impl From<ObjectDetectionImageSegmentation> for AvailableOnnxModel {
fn from(model: ObjectDetectionImageSegmentation) -> Self {
AvailableOnnxModel::Vision(Vision::ObjectDetectionImageSegmentation(model))
}
}