I want to train Mask-RCNN on my own dataset. I already have the segmented images (ground truths) of leaves which look something like the image below:
How can I load the dataset for training Mask RCNN?
Since Mask RCNN is pre-trained on COCO dataset,you need to train it with these images. For that purpose you have to label them and train it. Since a mask is involved use a tool such as VGG annotator to do the necessary annotation and labelling, it will generate a json file depending on your classes. Later based on your requirement you have to run the .py files for your classes, train and then generate it for testing.
You will have to convert this to TFrecords for the MASK RCNN model to be able to read the image and its annotations. Please refer this medium article 'https://medium.com/#vijendra1125/custom-mask-rcnn-using-tensorflow-object-detection-api-101149ce0765'
You can use coco annotations ( here is an example ) to annotate your dataset and then just run it like you use coco dataset.
Also you can check this code : https://github.com/matterport/Mask_RCNN/blob/master/samples/shapes/train_shapes.ipynb
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The labels for YOLO is like [class, x , y, width, height] . Since the dataset is very large, is there any shortcut to generate the labels for YOLO, or we have to hardcode them through measurement?
Method 1: Using Pre-trained YOLOv4 models.
YOLOv4 models were pre-trained on COCO dataset. So, if your object(s) can be found in this list, then, you can use the pre-trained weights to pseudo-label your object(s).
To process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt, use: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
Method 2: Using Other Pre-trained Models. It's the same concept. Use other pre-trained models to detect your object (as long as they have trained their models on your object), then export/convert the labels to YOLO format.
Method 3: Use hand-crafted feature descriptors. Examples are shape detection, color-based detection, etc.
Method 4: Manual labelling. If everything else fails, do the labelling yourself or hire some data labelling services. Here's a list of tools that you can use if you want to label them yourself.
I want to train YOLO to only detect the class person. Therefor I downloaded the COCO dataset, adjusted the labels to only this class and changed the config files accordingly, I then trained YOLO by following the steps described in the section "Training YOLO on COCO" on this site https://pjreddie.com/darknet/yolo/.
But the mean average precision (map) with my trained weights for the class person is much worse than the map for the same class when I use the trained weights from the same page under the caption "Performance on the COCO Dataset". I was wondering what could be the reason for this, and which data was used to train the weights available at the homepage.
Probably there's something wrong when you modify the cfg file (classes, filters, etc). Anyway what's the purpose of your task? Do you really need to retrain the model, or you only need to filter 1 class and make detection?
If you want to filter the Person label only out of 80 classes, you can simply do this workaround method. You don't need to retrain the model, you just need to use the weight provided by the author on yolo website.
For easy and simple way using COCO dataset, follow these steps :
Modify (or copy for backup) the coco.names file in darknet\data\coco.names
Delete all other classes except person
Modify your cfg file (e.g. yolov3.cfg), change the 3 classes on line 610, 696, 783 from 80 to 1
Change the 3 filters in cfg file on line 603, 689, 776 from 255 to 18 (derived from (classes+5)x3)
Run the detector ./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/your_image.jpg
For more advance way using COCO dataset you can use this repo to create yolo datasets based on voc, coco or open images. https://github.com/holger-prause/yolo_utils .
Also refer to this : How can I download a specific part of Coco Dataset?
It would be much simpler, IMMO, to use the pretrained weights with the presupplied *.names, *.data and *.cfg. Then you run the detector for single images or for a list of image file names in the mode (I do not remember the details) where YOLO outputs a list of detections in the form of class name, confidence and bbox. You then simply ignore anything other than the "person" class.
I have created text classification model(.pb) using tensorflow. Prediction is good.
Is it possible to check the sentence using for prediction is already used to train the model or not. I need to retrain the model when new sentence is given to model to predict.
I did some research and couldn't find a way to get the train data only with the pb file because that file only stores the features and not the actual train data(obviously),but if you have the dataset,then you can easily verify duh....
I don't think you can ever find the exact train data with only the trained model,cause the model only contains the features and not the actual train data
I try train ssd-mobilenet in my own dataset :
training image : 3400 with size :1600*1200
test set :800 with size :1600 *1200 tensorflow -gpu :1.13.1 gpu :4GB cuda 10.0 cudnn 7
object: road damage like aligator crack but after 197000 step my training loss cannot go down 2.
I have 2 questions
Should I normalize my training and set image before before using pretrained model like ssd_mobilenet?
If yes
Should I annotate images normalized or not ?
I need really helps. Thanks in advance
Should I normalize my training and set image before before using pretrained model like ssd_mobilenet?
No. Assuming you define your training pipeline correctly (see the examples in the TF Models repository), the Object detection API will take care of defining the appropriate image transformations (scaling, padding, normalization, etc) required in order to make the input compatible with the model.
I used the following documentation to train my own model to classify flowers as described there:
https://github.com/tensorflow/models/tree/master/inception#how-to-train-from-scratch
bazel-bin/inception/flowers_train --batch_size=32 --train_dir=/tmp/flowers_train --data_dir=/tmp/flowers_data
I specified --max_steps=30 only to see if I can use the model as expected for classification afterwards.
After these training steps I get the following files:
model.ckpt-29.data-00000-of-00001
model.ckpt-29.index
model.ckpt-29.meta
Unfortunately I actually don't know how to use these three files for image classification. Is there any example showing the necessary steps?
There's a section on how to evaluate (https://github.com/tensorflow/models/tree/master/inception#how-to-evaluate). It will use the saved model (those three files) to classify images and test it against the ground truth labels. You can dig into the code (models/inception/inception/inception_eval.py) to see how it loads and does the raw inference.