RCNN | tflearn implementation of the RCNN model | Machine Learning library

fast rcnn is an algorithm for object detection in images, in which we feed to neural network an image and it output for us a list of objects and its categories within the image based on list of bounding boxes called "ground truth boxes". the algorithm compare the ground truth boxes with the boxes generated by the fast-rcnn algorithm and only keep those that sufficiently overlapped with the gt boxes. the problem here that we must resize the image to be fed into CNN, my question is, should us resize also the ground truth boxes before the comparaison step, and how to do that? tanks to reply.

Tensorflow issue google colab : module 'tensorflow._api.v1.compat.v2' has no attribute 'internal' I am running a MASK RCNN model on google colab With tensorflow 1.15 and keras 2.1.6 every thing work correctly but Today, I got this error: enter image description here

being new to Deep Learning i am struggling to understand the difference between different state of the art algos and their uses. like how is resnet or vgg diff from yolo or rcnn family. are they subcomponents of these detection models? also are SSDs another family like yolo or rcnn?

Similar to this question:

Where can I find model.ckpt in faster_rcnn_resnet50_coco model? (this solution doesn't work for me)

I have downloaded the ssd_resnet152_v1_fpn_1024x1024_coco17_tpu-8 with the intention of using it as a starting point. I am using the sample model configuration associated with that model in the TF model zoo.

I am only changing the num classes and paths for tuning, training and eval.

With:

I have used code of this blog "https://learnopencv.com/deep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c/" Titled Deep learning based Object Detection and Instance Segmentation using Mask R-CNN in OpenCV in python . I am using live stream and want to do object detection and instance segmentation on that and modified the code below rest is same as explained in the blog

I've started annotating using LabelImg tool and drawing boxes but I have too many pictures in the images (like lots of grapes in the image). For better trained model, is it required to box all pictures or is it okay to leave some?

I am trying to train a Faster RCNN model.

example: Thanks

I am trying to train the torchvision Faster R-CNN model for object detection on my custom data. I used the code in torchvision object detection fine-tuning tutorial. But getting this error:

I use the Tensorflow Object Detection API to create an AI for Faster-RCNN. GitHub:Tensorflow/models

What kind of resizing function does "keep_aspect_ratio_resizer {" in the config file have?

I prepared images of 1920 x 1080 pixels and set "min dimension:" and "max dimension:" described immediately after "keep_aspect_ratio_resizer {" in the config file to 768 respectively.

In this case, the 1920x1080 pixel image would be resized to 768x768 pixels and input to the CNN. At this time, will the original ratio of the image (16: 9) be maintained? Namely, when the image is resized to 768x768 pixels, will the long sides of the image be converted to 768 pixels and black bars will be added in the margin of the image?

Or does the image ratio change from 16: 9 to 1: 1 and become contort when this setting?

If anyone knows about this, please let me know.

Thank you!

I have trained a model in PyTorch - an RCNN for text classification. The model has very high precision and recall, but I may eventually receive new documents with text unlike what I used to train, validate, or test the model.

I would like to add new text samples to the model without retraining the model from the beginning. This is desirable because I may lose access to some of the text used for initial training.

If it is not possible to add samples (documents), is it possible to train a new model on only the new samples and then somehow combine the original model and the new model? How?

Here is what my model looks like.