Mmdetection: Object detection and instance segmentation toolbox.

We will start by discussing why this post will help you if you are learning object detection or starting to master MMDetection. Feature selective anchor-free module for single-shot object detection.

The model’s config file can be edited in a text-based format by referring to the instructions on the MMDetection documentation site; a detailed description of the config file can be found here. The config file is a text file written in Python, where variable names and data structures are set according to the MMDetection definition rules.

• On an individual scale, studies including species classification, crop disease detection, and weed detection are well researched (Christin et al., 2019; Hasan et al., 2021; Liu and Wang, 2021).
• More convolution layers in bbox head will lead to higher performance.
• Benchmarks, such as COCO, play a crucial role in object detection.
• We benchmark different methods on COCO 2017 val, including SSD, RetinaNet, Faster RCNN, Mask RCNN and Cascade R-CNN, Hybrid Task Cascade and FCOS.
• Since test files don’t really have an annotation we will just point it to the val annotations for now.

Only one team’s model passed the target accuracy requirement for grading and became the champion of the embedded deep learning object detection model contest, which the winner is team R.JD. Experimental results demonstrate that the proposed approach can significantly improve the average precision of object detection on the subset of the COCO2017 dataset. Additionally, model_weight can be provided for these config files.

Use models like Faster RCNN, YOLOv3 with MobileNet backbones, FCOS for anchor free object detection, and many more models from the MMDetection toolbox. In step 1, we used U2-Net trained on the DUTS dataset (Wang et al., 2017) (0th-trained U2-Net) obtained from the corresponding GitHub repository (Qin et al., 2021) for leaf segmentation of cucumber leaf images. In step 2, the images whose nearly complete area was detected as a salient object or without detection were discarded. In step 3, we used the remaining images and detection results (i.e., mask images) to retrain U2-Net. In step 4, we used the trained U2-Net from step 3 to perform salient object detection for the cucumber leaf images again. Then, we repeated steps 2–4 to retrain U2-Net five times, obtaining the 5th-trained U2-Net, and training was performed using the CUI for efficiency.

Furthermore, we explore different number of convolution layers for bbox head. The pre-trained model checkpoints from MMdetection should now be downloaded for further adjustment and inference. MMDetection toolbox was created as a Python codebase specifically for problems involving object identification and instance segmentation. The MMdetection models can be trained using the fit method. Model – the name of the models from the list of supported models. The next thing to do is to configure the model and the dataset.

2 Object Detection And Instance Segmentation

In 2018, the MMdet team won the COCO object detection challenge. Their codebase, which is built with Pytorch, has gradually evolved to include various models and methods. Although the detection task can be complex, the creators of MMDetection decomposed the models into different general components – Backbone, Neck, DenseHead, ROIExtractor, and ROIHead. With this abstraction, the library is able to provide a multitude of single-stage (e.g. GHM, FCOS), two-stage (e.g. Double-Head R-CNN), multi-stage (e.g. Cascade R-CNN), and other detection models.

• Upon launching the GUI, the user is prompted to select an analysis task; after this selection, the analysis screen is displayed.
• The SugarBeets2016 dataset has 11,552 RGB images captured under fields and the annotations for sugar beets and weeds.
• There is an official conversion script available, which can be used to export MMDetection models to ONNX format.
• We import the inference_detector function from the mmdet.apis module.

We constructed Faster R-CNN, YOLOv3, SSD, and RetinaNet with MMDetection backend and Faster R-CNN and RetinaNet with Detectron2 backend for training and validation with the GWHD dataset. To initialize each architecture, we retrieved the pretraining weights from the GitHub repositories of MMDetection and Detectron2. As a case study of salient object detection with JustDeepIt, we use the Plant Phenotyping Dataset, a popular benchmark dataset for plant segmentation (Minervini et al., 2015; Minervini et al., 2016).

Open-mmlab / Mmdetection

The 0th-trained U2-Net failed to detect leaves in images containing multiple leaves. In contrast, the 5th-trained U2-Net successfully detected the main salient leaf in every image . Thus, even without annotations, we built a model for leaf segmentation using existing techniques.

The training took approximately 6.5 hours with four processors but without any graphics processor. We developed the JustDeepIt software supporting GUI and CUI to train models and perform inference for object detection, instance segmentation, and salient object detection. JustDeepIt can be applied to many biological problems, such as wheat head detection, plant segmentation, and leaf segmentation. In addition, it provides an intuitive solution for biologists lacking programming experience and machine learning expertise, simplifying implementation compared with conventional programming schemes. Scikit-image and ImageJ require users to set thresholds manually for multiple color spaces to segment leaf areas. Therefore, if an image consists of various phenotypes of plants, for example, plants with green and red leaves due to some stress, simultaneously segmenting both plants may be challenging. PlantCV supports building task-specific machine learning models for instance segmentation.

This will also help you figure out the best one that you may want to fine-tune on your own dataset. Mixed precision training results of MMDetectionon different models.