I have been trying to retrain YOLOv3 on a custom dataset. I saved the jpg images and their corresponding txt annotation files in the same directory. I have set the my .data file, .names file and .cfg file appropriately as suggested in many tutorials online. Quite frustratingly, I have been running into the problem where it says Couldn't open file: <some-path>/labels/<some file>.txt. What is annoying here it seems to be looking for .txt files in some labels directory which neither exists, nor did I mention it anywhere. All my .jpg and .txt files are in a directory named images located at the same level as where the system is looking for this labels directory.
What is further annoying is if I do separate .txt files into a labels directory which is where the yolo darknet is looking for, this error goes away but the training never starts.
I have tried many different ways of specifying the paths, using different models, cfg files etc, but all in vain. Please help someone.
After making many attempts from different angles, I found out the right way. The answer to the first question of organizing images and labels is contrary to what most tutorials online suggest. labels should be located in a separate directory from the images. The path to the each .txt file should differ from their corresponding .jpg only in the word images. For example, if the path to an image is <path/to/somewhere/images/somewhere/xyz.jpg>, the path to its corresponding label file should be <path/to/somewhere/labels/somewhere/xyz.txt>.
As an answer to the second part where the training does not start, make sure to use argument -clear 1 at the end of the darknet training command, i.e. ./darknet detector train cfg/data_file.data cfg/cfg_file.cfg yolov3.weights -clear 1.
Remember, in your *.data file, you have the following settings:
classes = [Your number of classes]
train = data/train.txt
valid = data/test.txt
names = data/obj.names
backup = backup/
You should have data/train.txtand data/test.txt, which is a text file containining the directory list of your images.
For example, if you put all of your images and txt files at data/obj, the txt file should contain:
data/obj/1.jpg
data/obj/2.jpg
.
.
(and so on)
Then, YOLO will automatically check the corresponding label of the images, which should be having the same name (in this case: 1.txt, 2.txt, ...)
Reference:
(No. 3 and 4 in https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects)
Related
I was using Google Colab to train Yolo-v3 to detect custom objects. I'm new to Colab, and darknet.
I used the following command for training:
!./darknet detector train "/content/gdrive/My Drive/darknet/obj.data" "/content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg" "/content/gdrive/My Drive/darknet/backup/yolov3-PID_final.weights" -dont_show
The training finished as follows, and it didn't display any details of the epochs (I don't know how many epochs actually run). Actually, it took very short time until it displayed Done!, and saved the weights as shown in the above image
Then, I tried to detect a test image with the following command:
!./darknet detect "/content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg" "/content/gdrive/My Drive/darknet/backup/yolov3-PID_final.weights" "/content/gdrive/My Drive/darknet/img/MN 111-0-515 (45).jpg" -dont-show
However, I got the following error:
Error: in the file data/coco.names number of names 80 that isn't equal to classes=13 in the file /content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg
Even, the resulting image didn't contain any bounding boxes, so I don't know if the training worked or not.
Could you pls advise what might be wrong with the training, and why the error is referring to coco.names, while I'm using other files for names, and configuration?
You did not share the yolov3-PID.cfg, obj.data and coco.names. I am assuming coco.names contain 80 classes as in the repo.
The error likely is in obj.data, where it seems your goal here is to detect 13 custom objects. If this is the case, then set classes=13, also replace names=data/coco.names with names=data/obj.names. Here, obj.names file should contain 13 lines for the custom class names. Also modify yolov3-PID.cfg to contain same amount of classes.
I suggest using this repo below if you are not already using this. It contains google colab training and inference script for yolov3, yolov4.
Here are the instructions for custom object detection training.
Nice work!!! coming this far. Well, everything is fine, you just need to edit the data folder of the darknet. By default it's using coco label, go to darknet folder --> find data folder --> coco.names file --> edit the file by removing 80 classes(in colab just double click to edit and ctrl+s to save) --> Put down your desired class and it's done!!!
i was having the same problem when i was training custom model in colab.
i just cloned darknet again in another folder and edited coco.name and moved it to my training folder. and it worked!!
In tensorflow, the training produced the following files:
checkpoint
model.ckpt-10000.meta
model.ckpt-10000.data-00000-of-00001
model.ckpt-10000.index
model.ckpt-11000.meta
model.ckpt-11000.data-00000-of-00001
model.ckpt-11000.index
model.ckpt-12000.meta
model.ckpt-12000.data-00000-of-00001
model.ckpt-12000.index
model.ckpt-8000.meta
model.ckpt-8000.data-00000-of-00001
model.ckpt-8000.index
model.ckpt-9000.meta
model.ckpt-9000.data-00000-of-00001
model.ckpt-9000.index
I am interested in creating a .pb file from the output generated training; however, from the examples I have seen, it requires one set of intermediate output files. How do I merge all the output set files into a single .pb?
What you are trying to do does not make sense (at least to me). I recommend you to read about these checkpoint files here and here at first.
In short, checkpoint file just tells you what is the latest model. The .meta file stores info about your graph structure, .data stores values for variables and .index stores key/value pairs which have info where the values for each parameter can be found in .data files.
All your files look like model.ckpt-xxxx. This xxxx is the step number. So you have snapshots of training at different steps. And this is why it does not make sense to combine the value of the variable at step 9000 with the value at step 11000. Also .meta files are probably all the same.
I am working on image classification problem using Keras framework. This is binary classification problem and I have 2 folders training set and test set which contains images of both the classes. I don't have separate folder for each class (say cat vs. dog). Keras ImageDataGenerator works when we have separate folders for each class (cat folder & dog folder). But I have all the images in single folder training set and I am not understanding how to proceed further. Kindly suggest how to load the images.
I also have 2 CSV files - train.csv and test.csv. train.csv contain 2 columns namely image_id and class_name. test.csv contains image_id. Note that image_id is matching with the name of files in the images folders.
The latest versions of ImageDataGenerator have a method called flow_from_dataframe, which does exactly what you want.
Basically it is used by first loading your CSV file in a pandas DataFrame, instantiate a ImageDataGenerator and then call flow_from_dataframe with three important parameters:
directory: Folder where your data lives.
x_col: Column in the DataFrame that contains the filenames inside the directory that correspond to your training/testing data.
y_col: Column in the DataFrame corresponding to the labels that will be output by the generator.
Then you use this generator as any other, by calling fit_generator. More information and examples are available here.
I am trying to do a Deep Learning project by using Tensorflow.
Each of my data sets contains 2 files( PNGimage file + TXTvectors file ), where are put in different folders as follow:
./data/image/ #Folders contains different size of images
./data/vector/ #Folders contains vectors of corresponding image
#For example: apple.png + apple.txt
The example content of vector shows as follow:
10.0,2.5,5,13
And since image size are different, the resize and some transformation apply on vectors are required. It is important to make sure that I can do these processing during Tensorflow is running. Is there any good way to manage this kind of datasets?
I referred to a lot of basic tutorial however most of them are not so many details about arrange customized data input and output. Please give me some advice!
I recommend you to take a look at TFRecords and queues. Basically the idea is the following: you resize all your images to the same format and store them together with your txt vectors in one TFRecord file. This is done separately before you run your model.
When you create your model you create a queue which reads data from the TFRecord file and feeds it to your model.
Running the model out of the box generates these files in the data dir :
ls
dev-v2.tgz newstest2013.en
giga-fren.release2.fixed.en newstest2013.en.ids40000
giga-fren.release2.fixed.en.gz newstest2013.fr
giga-fren.release2.fixed.en.ids40000 newstest2013.fr.ids40000
giga-fren.release2.fixed.fr training-giga-fren.tar
giga-fren.release2.fixed.fr.gz vocab40000.from
giga-fren.release2.fixed.fr.ids40000 vocab40000.to
Reading the src of translate.py :
https://github.com/tensorflow/models/blob/master/tutorials/rnn/translate/translate.py
tf.app.flags.DEFINE_string("from_train_data", None, "Training data.")
tf.app.flags.DEFINE_string("to_train_data", None, "Training data.")
To utilize my own training data I created dirs my-from-train-data & to-from-train-data and add my own training data to each of these dirs, training data is contained in the files mydata.from & mydata.to
my-to-train-data contains mydata.from
my-from-train-data contains mydata.to
I could not find documentation as to using own training data or what format it should take so I inferred this from the translate.py src and contents of data dir created when executing translate model out of the box.
Contents of mydata.from :
Is this a question
Contents of mydata.to :
Yes!
I then attempt to train the model using :
python translate.py --from_train_data my-from-train-data --to_train_data my-to-train-data
This returns with an error :
tensorflow.python.framework.errors_impl.NotFoundError: my-from-train-data.ids40000
Appears I need to create file my-from-train-data.ids40000 , what should it's contents be ? Is there an example of how to train this model using custom data ?
blue-sky
Great question, training a model on your own data is way more fun than using the standard data. An example of what you could put in the terminal is:
python translate.py --from_train_data mydatadir/to_translate.in --to_train_data mydatadir/to_translate.out --from_dev_data mydatadir/test_to_translate.in --to_dev_data mydatadir/test_to_translate.out --train_dir train_dir_model --data_dir mydatadir
What goes wrong in your example is that you are not pointing to a file, but to a folder. from_train_data should always point to a plaintext file, whose rows should be aligned with those in the to_train_data file.
Also: as soon as you run this script with sensible data (more than one line ;) ), translate.py will generate your ids (40.000 if from_vocab_size and to_vocab_size are not set). Important to know is that this file is created in the folder specified by data_dir... if you do not specify one this means they are generated in /tmp (I prefer them at the same place as my data).
Hope this helps!
Quick answer to :
Appears I need to create file my-from-train-data.ids40000 , what should it's contents be ? Is there an example of how to train this model using custom data ?
Yes, that's the vocab/ word-id file missing, which is generated when preparing to create the data.
Here is a tutorial from the Tesnorflow documentation.
quick over-view of the files and why you might be confused by the files outputted vs what to use:
python/ops/seq2seq.py: >> Library for building sequence-to-sequence models.
models/rnn/translate/seq2seq_model.py: >> Neural translation sequence-to-sequence model.
models/rnn/translate/data_utils.py: >> Helper functions for preparing translation data.
models/rnn/translate/translate.py: >> Binary that trains and runs the translation model.
The Tensorflow translate.py file requires several files to be generated when using your own corpus to translate.
It needs to be aligned, meaning: language line 1 in file 1. <> language line 1 file 2. This
allows the model to do encoding and decoding.
You want to make sure the Vocabulary have been generated from the dataset using this file:
Check these steps:
python translate.py
--data_dir [your_data_directory] --train_dir [checkpoints_directory]
--en_vocab_size=40000 --fr_vocab_size=40000
Note! If the Vocab-size is lower, then change that value.
There is a longer discussion here tensorflow/issues/600
If all else fails, check out this ByteNet implementation in Tensorflow which does translation task as well.