How can I then deploy the trained model on roboflow pls ? It is said it is not supported and if i canhed to yolov8 there is a depedency issue : ultralytics==8.0.196 is required whereas ultralytics==8.3.2 is installed
Hello, I have some questions about neural network processing with YOLO... I have a scenario where I need to identify coffee boxes whose only difference is the color to identify one type of coffee as different from another. I have always believed that for performance reasons, network training converts images to grayscale to work on only 1 color channel and does the same when inferring new images. Is this understanding correct? Is it possible to train the models considering the colors, the 3 RGB channels? Or would the best option be to identify the objects (bounding boxes) and perform post-processing with OpenCV, for example, in the bouding box region to identify the closest color? Thanks
I think that most neural networks use all color channels. A cool example is my football AI project: th-cam.com/video/aBVGKoNZQUw/w-d-xo.html I think it's safe to assume that the model distinguishing players from referees and goalkeepers is largely based on the color of their uniforms. I think that a single-stage pipeline makes a lot of sense, especially as a POC version. A multi-stage solution also makes a lot of sense, but it is prone to errors due to, for example, changes in lighting.
I am working on a project and considering using Roboflow. In my project, I will classify the geometric shapes (triangle, quadrilateral, circle, zigzag, etc.) found on shoe soles. At this point, I will label them using object detection. What is your suggestion on this?
@@Roboflow I am considering labeling and classifying the geometric shapes found on shoe soles using the object detection method. I plan to use thousands of shoes for my project. At this point, would it be appropriate to use object detection for this purpose? Is nested labeling a suitable approach, such as labeling both a rectangle and a star inside it?
@@Roboflow can yolo11 do this? I've just never seen Yolo distinguish people's faces. Usually it just distinguishes between a person/dog, etc. i work with esp32cam, not raspberry.
It can as long as thee set of people you want to detect is small. YOLO is only efficient up until 80-100 classes, so it's not really applicable in large-scale face recognition systems, but it can work for small problems like this.
Tutorial starts at 14:21
How can I then deploy the trained model on roboflow pls ? It is said it is not supported and if i canhed to yolov8 there is a depedency issue :
ultralytics==8.0.196 is required whereas ultralytics==8.3.2 is installed
We are still working on it ;) stay tuned
Hello, I have some questions about neural network processing with YOLO... I have a scenario where I need to identify coffee boxes whose only difference is the color to identify one type of coffee as different from another. I have always believed that for performance reasons, network training converts images to grayscale to work on only 1 color channel and does the same when inferring new images. Is this understanding correct? Is it possible to train the models considering the colors, the 3 RGB channels? Or would the best option be to identify the objects (bounding boxes) and perform post-processing with OpenCV, for example, in the bouding box region to identify the closest color? Thanks
you may turn off the color data augmentation. For coco object detection, the NN should neglect color info。
I think that most neural networks use all color channels. A cool example is my football AI project: th-cam.com/video/aBVGKoNZQUw/w-d-xo.html I think it's safe to assume that the model distinguishing players from referees and goalkeepers is largely based on the color of their uniforms.
I think that a single-stage pipeline makes a lot of sense, especially as a POC version. A multi-stage solution also makes a lot of sense, but it is prone to errors due to, for example, changes in lighting.
Thank you so much for stream 🎉😊
I am working on a project and considering using Roboflow. In my project, I will classify the geometric shapes (triangle, quadrilateral, circle, zigzag, etc.) found on shoe soles. At this point, I will label them using object detection. What is your suggestion on this?
Sounds great! Any specific problems you are facing?
@@Roboflow I am considering labeling and classifying the geometric shapes found on shoe soles using the object detection method. I plan to use thousands of shoes for my project. At this point, would it be appropriate to use object detection for this purpose? Is nested labeling a suitable approach, such as labeling both a rectangle and a star inside it?
I want to put a camera at the front door and identify visitors - my family/stranger. What is the best way to do this?
Make some photos. Create dataset (probably 100 images for start). Train a nano model. Deploy raspberry pi.
@@Roboflow can yolo11 do this? I've just never seen Yolo distinguish people's faces. Usually it just distinguishes between a person/dog, etc. i work with esp32cam, not raspberry.
@@Roboflow I will send the photos to the server and then pass them through the neural network
If you don't plan to run the inference locally, the code you'll run on device will be really simple.
It can as long as thee set of people you want to detect is small. YOLO is only efficient up until 80-100 classes, so it's not really applicable in large-scale face recognition systems, but it can work for small problems like this.
Can I use Yolo11 for tracking
Architecture diagram sir?
What's the secret roboflow key
YOLO11 notebook contains information on how to retrieve it