I am trying to implement YOLOv4 with this command
!./darknet detector train data/obj.data /content/drive/MyDrive/yolov4/darknet/cfg/yolov4-custom.cfg /mydrive/yolov4/training/yolov4-custom_last.weights -dont_show -map
I've looked every post here consisting this problem. However, suggestions such as
1) Change your colab path as
!ln -s /content/drive/My\ Drive/ /mydrive
which is basicly changing some embedded keywords to another. Did not work
Also checked every path, change even the most basics with original path (copied from Colab -> Drive). Still have the error.
I've used a video by techzizou, credits from here.
So, what can be the problem? Thanks a lot!
Related
I am trying to run deep-sort for real-time object tracking on yolov4-tiny model on webcam from this github repository.
https://github.com/theAIGuysCode/yolov4-deepsort
But there is only command for yolov4 for real time object detection using webcam. How can I modify the code and what will be the command to run on yolov4-tiny to run on webcam for real time object detection.
I will be glad if you suggest me some other way to run object tracking(less resource hungry way) or in tensorflow lite.
The answer is on the setup page https://github.com/theAIGuysCode/yolov4-deepsort. You don't need to make changes you just need to get the right weights and then run it pointing to the correct weights like:
{#Run yolov4-tiny object tracker
python object_tracker.py --weights ./checkpoints/yolov4-tiny-416 --model yolov4 --video ./data/video/test.mp4 --output ./outputs/tiny.avi --tiny}
You can look at the yolov4.py code and you will seen how it checks for normal or tiny when you give it the command line string see https://github.com/theAIGuysCode/yolov4-deepsort/blob/master/core/yolov4.py.
Please i need you help concerning my yolov5 training process for object detection!
I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.
The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.
This is what i get when the training process starts
This the last message error when the training finish
I think the problem comes maybe from here but i don't know how to fix it, i used the code of yolov5 team as it's in the tuto
The training continue without any problem but the map and precision remains 0 all the process !!
Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
This is what I would do to troubleshoot it. - Run your code on collab because the environment is proven to work well - Confirm that your labels look good and are setup correctly. Can you checked to ensure the classes look right? In one of the screenshots it looks like you have no labels
Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to #alexheat
I'm using Yolov5 for my custom dataset too. This problem might be due to the directory misplacement.
And using different version of Pytorch will not be a problem. Anyway you can try using the version they mentioned in 'requirements.txt'
It's better if you run
cd yolov5
pip3 install -r requirements.txt
Let me know if this helps.
I am trying to train custom data set that consists of currency. i followed a youtube tutorial, made the same folder structure.
I am using google colab for free gpu and darknet. everytime i run data for training it finishes within seconds without any error and the final output says "608 x 608 create 6 permanent cpu threads"
the tutorial i followed shows the training of dataset but mine is keep getting stuck at this message.
I'm using yolov3 to train my dataset, followed every step of changing things in makefile. Also the train.txt and test.txt files stays empty too. (sorry for my bad english)
Below attached screenshot of the message i get when i try to train my model.
SOLVED : the issue was my train.txt file was empty because it wasn’t getting any image paths, soo i changed absolute path of my images folder to relative path and it saved all the images paths in train.txt file which resulted in activation of data training(sorry for my bad english)
I am trying to create a mobile app that uses object detection to detect a specific type of object. To do this I am starting with the Tensorflow object detection example Android app, which uses TF2 and ssd_mobilenet_v1.
I'd like to try Few-Shot training (Colab link) so I started by replacing the example app's SSD Mobilenet v1 download with the Colab's output file model.tflite, however this causes the the app to crash with following error:
java.lang.IllegalStateException: This model does not contain associated files, and is not a Zip file.
at org.tensorflow.lite.support.metadata.MetadataExtractor.assertZipFile(MetadataExtractor.java:313)
at org.tensorflow.lite.support.metadata.MetadataExtractor.getAssociatedFile(MetadataExtractor.java:164)
at org.tensorflow.lite.examples.detection.tflite.TFLiteObjectDetectionAPIModel.create(TFLiteObjectDetectionAPIModel.java:126)
at org.tensorflow.lite.examples.detection.DetectorActivity.onPreviewSizeChosen(DetectorActivity.java:99)
I realize the Colab uses ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz - does this mean there are changes needed in the app code - or is there something more fundamentally wrong with my approach?
Update: I also tried the Lite output of the Colab tf2_image_retraining and got the same error.
The fix apparently was https://github.com/tensorflow/examples/compare/master...cachvico:darren/fix-od - .tflite files can now be zip files including the labels, but the example app doesn't work with the old format.
This doesn't throw error when using the Few Shots colab output. Although I'm not getting results yet - pointing the app at pictures of rubber ducks not yet work.
I was working around with Tensorflow's Image Recognition tutorial (https://www.tensorflow.org/tutorials/image_recognition)
It gave me the right answer when i run
python classify_image.py
But when I run
python classify_image.py --cat.jpg
It returns the same answer as before. I did put the image in the imagenet folder (same as classify_image.py)
You are passing the argument in a wrong way. Try:
python classify_image.py --image_file cat.jpg