Tobias you asked me to share a feedback after the end of my work. Please find it below .
1st : Kudos to Hasty team for such effective service to help in labelling work. The power of your AI assistants reveals after 250 processed images. They helped to accelerate the labelling work.
I annotated about a total 1070 images and produced about 26500 labels. Doing this without AI assistants would have been a true nightmare (This is not FAKEFEEDBACK!)
I also thank Hasty community.
Kudos to Hasnain and Treebeard, you provided guidance which helped me to understand aspects of ML training I wasn’t aware about. Attributes are important. They need to be thought at the very beginning of the project, have obvious meanings, and be added to the right classes. Your messages were decisive for my work.
2nd: to perform the ML training, I explored youtube to capture information.
Because I wanted to access the best technology to deliver the best results, I wanted to refactor my work environment from Yolov3 to Yolov5. (Remember I’m a beginner) Because the online tutorials about ML training were very well designed, I used Roboflow + Google Colab to do the work. It did the trick. In few hours, I managed to get an effective object detector based on Ultranatics tech demonstrator. (mAP of 76%). When it was the time to integrate the Yolov5 into my work environment, I was clueless : no tutorial, example, youtube videos processed this part of the SW development. I explored all the websites possible (Github, ultranatics, etc…) for hours. These efforts gave no satisfying outcome. The only given solution that exists needs an online link with some external webservice (that is probably free) Because my work environment is offline, I had to twist ultranatics offline demonstrator and rebuild my work environment around it to get what I wanted. I could have purchased some ultranatics support service hours but I gave up the idea. It was sad that I didn’t catch from the beginning that paying support was strongly recommended to being able to integrate YoloV5 in the manner I needed.
3rd: after the Yolov5 trial, I did the ML training with Roboflow, Google Colab and YoloV4. The ML training environement preparation was less easy than Yolov5’s one, but, in the end I got an mAP of 87% and even 90% on some classes. The cherry on the cake was when I could integrate the result of the ML Training in my work environment built initially around YoloV3 with just 3 changes.
Now I have an effective object detector able to answer my needs. \o/
In regards to the things to improve in Hasty, I propose ONE feature that I do think will help to avoid repetitive moments of pain.:
- ability to change attributes for a group of labels from the “labels editor (with the picture to be annotated)” and from the “Manual Review tool”.
During my work, I had to add 5 or 6 times new attributes in order to facilitate the IA work. The consequence is that I had to change the attributes of each label from the very beginning of the dataset. When you have 500 images to review 5 or 6 times, this is painful. Such feature to change attributes from the manual review tool or for a group of labels in the “label/picture editor” would be REALLY fantastic.
What will happen next: I plan to make bigger my dataset (potentially 500 additional pictures) in order to improve the ability of the object detector to capture small sized objects in pictures.