In the third lecture you explained in advance, jetpack sdk 4.4 version was installed, but in the current lecture, it is explained in 4.6 version. Is there any problem if you look at the previous lecture and use 4.4 version to install pytorch through the current video and follow the video?

Please let me know how to do it with the custom dataset downloaded from roboflow.

Aashiqui 2 - Aarohi ❌️ Code with Aarohi ❤

Hello, I appreciate your work. However, when I went a little deeper into the project, I saw that the c3d model performed very poorly in alternative videos. Is there another source you can give for using other models such as i3d models? I wanted to ask you because it is impossible for me to train this i3d model on my own computer. So I am waiting for your answer 🙏

Can we convert yolov10 custom trained model to quantised tflite model

Amazing video

Very nice

Thank you so much. it really helped me

hi, nice video detections = sv.Detections.from_coco_annotations(coco_annotation=annotations) it says AttributeError: type object 'Detections' has no attribute 'from_coco_annotations'

I really enjoyed your video. I found it to be very concise.

Great Content

Thank you for the good video. I did what you told me to do and also checked that the video came out of the camera. But the window where the camera comes out is so big that there is no 'x' button to close the window. Is there any way to make the camera window appear small on the monitor like you? The camera window I came out of takes up about half of the monitor.

Dear ma,your video is excellent,do you have it with data custom datasets please..can you share with me the link with same method but with custom datasets please.. Waiting to read from you soon

Thanks

thanks for such easy tutorial on image classification mam.... worth watching your channel

Thanks a lot for the video. I have a question. @CodeWithAarohi why did you use kotlin instead of java like in yolov5 android app?

There is any solution for this?: RuntimeError: CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

could you please create YOLOv8 android app that use onnx

i am following the same porcess as you said but when i open the jupyter notebook it is not asking password for keyring and the python example folder is missing in that when i open the jupyter notebook and i'm getting the error as CAN'T INTIALIZE NVRM CHANNEL what may be the issue? please reply for this as this issue is unsolved for many people

hi, Good explanation but at the end, when you explained what would be the input to the decoder's masked multi-head attention, you fumbled and didn't explain clearly. But the rest of the video was very good.

Very nice explanation, thank you! I tried to follow the tutorial but got stuck when i got, "'yolo' is not recognized as an internal or external command, operable program or batch file." while i tried to predict, "!yolo task=detect mode=predict conf=0.25 save=True......".... any comments on that?

Thank you for the explanation. However, when i have applied 'python segment/predict.py ....'. I got this error: AttributeError: 'list' object has no attribute 'shape'. Any solution???

🎯 Key Takeaways for quick navigation: YOLOv10 introduces a feature called "NMS free training" to avoid duplicate bounding boxes for the same object, reducing postprocessing time and computational resources. Spatial Channel Decoupled Down sampling in YOLOv10 separates spatial and channel operations to make downsampling more efficient, using pointwise and depthwise convolutions. Rank Guided Block Design in YOLOv10 adjusts model stages based on redundancy levels, improving efficiency by allocating compact inverted blocks where necessary. Lightweight classification heads in YOLOv10 are designed to be efficient in assigning labels without compromising accuracy. Made with HARPA AI

Hello Madam, Thank you for your continuous support to students like me. I'm curious if you've implemented real-time YOLO image detection using a mobile phone's back camera from a browser before. Specifically, when a user opens the Flask web app and clicks a button, the back camera should automatically open and display live predictions. Currently, I can achieve this with a laptop webcam by setting the source to 0, but it doesn't work on a mobile phone. Do you have any sample projects or resources on how to do this? Your guidance means a lot.

Hi, first of all thank u for this amazing content. I want to ask you a question. I am trying to create a model based on YoloV8 to detect the color of the traffic light. I am having an issue that when I use the train model with the camera in video mode, the model takes a while but detect the traffic light but when I try to detect only by sending one frame, the detection is null. I do not understand why when you send multiple frames it gives you a better detection. Could you help me with this? Thank u

I'm trying to implement the model with my custom java code but can't seem to scalep : private List<ObjectDetectionResult> processOutput(float[] outputData) { allResults = new ArrayList<>(); int numDetections = outputData.length / (NUM_VALUES_PER_DETECTION + labels.size()); for (int i = 0; i < numDetections; i++) { if(outputData[i * 8 + 4] > NMS_THRESHOLD){ int detectionOffset = i * (NUM_VALUES_PER_DETECTION + labels.size()); float confidence = outputData[detectionOffset]; if (confidence < CONFIDENCE_THRESHOLD) continue; float centerX = outputData[detectionOffset] * 640; float centerY = outputData[detectionOffset + 1] * 640; float width = outputData[detectionOffset + 2] * 640; float height = outputData[detectionOffset + 3] * 640; float scaleFactorX = surfaceView.getWidth() * 1f / 640; float scaleFactorY = surfaceView.getHeight() * 1f / 640; // Calculer les positions relatives à l'image redimensionnée float left = centerX - width / 2; float top = centerY - height / 2; float right = centerX + width / 2; float bottom = centerY + height / 2; RectF boundingBox = new RectF(left, top, right, bottom); OR // Convertir les coordonnées du modèle en pixels de l'image d'entrée (640x640) float centerX = outputData[detectionOffset] * 640; float centerY = outputData[detectionOffset + 1] * 640; float width = outputData[detectionOffset + 2] * 640; float height = outputData[detectionOffset + 3] * 640; // Calculer les positions relatives à l'image redimensionnée float left = centerX - width / 2; float top = centerY - height / 2; float right = centerX + width / 2; float bottom = centerY + height / 2; // Mettre à l'échelle les coordonnées en fonction des dimensions de la surfaceView left *= scaleFactorX; top *= scaleFactorY; right *= scaleFactorX; bottom *= scaleFactorY; RectF boundingBox = new RectF(left, top, right, bottom); doesn't work

Hey i installed yolov7 but when i run the command for python train.py ……. P5.yaml i got the error illegal instruction (core dumped)

isn't that a yolov8?

What's about the extra class? and i think that only the extra class is used for the classification. Please could you explain this point?

Good comparison! Well done! 👏🏻

Thankyou, extremely useful video, better than most. Keep your excellent presentations.

I am getting this error after finishing training the model when I want to predict (inference): i !yolo task=detect mode=predict conf=0.25 save=True model=runs/detect/train/weights/best.pt source=test_images_1/veh2.jpg 32 try: 33 # Issuing `None` to a generator fires it up 34 with ctx_factory(): ---> 35 response = gen.send(None) 37 while True: 38 try: ... --> 216 bs = prediction.shape[0] # batch size 217 nc = nc or (prediction.shape[1] - 4) # number of classes 218 nm = prediction.shape[1] - nc - 4 AttributeError: 'dict' object has no attribute 'shape' Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

I got error when i put the requirements.txt command. it says could not find a version that satisfies the requirement gitpython

Great, as always ! Thank you ! I have one stupid question...Why Ultralytics says it is version ultralytics-8.1.34, i expect v10 will be V10?? Can you explain? Thanks !

Amazing👍

could you please make a video on yolo world custom dataset training

Hi, I facing some error while I try to run this code in anaconda ! can you please share a video how to create a new env for that using GPU

Hi, thanks for the excellent demostration!

Thank you for the clear explanation. Can you do one incorporating DeepSort tracking ?

suggestion: consider using jupyterlab and a dark theme.

Interesting!

Can you please guide me why other weights of YOLOV10 is not working on colab because small ansd nano are working.

Hi Aarohi, thank you for the video. Wondering I used Git clone to download bbox-label-tool package, but inside it I could not see the three files you said "bbox tool""convert""and proces", wondering if they have been removed? thank you.

I love your channel. Really great stuff. If you can, I suggest buying an external microphone. Improved sound quality would do wonders to improve the quality of the videos.

i have a little problem. why jupyter notebook all text display same color. this issue same as in my computer. do you know a way fix this ?

🥰🥰

where can I find a license plate dataset for Italy? or the one you used works anyway?

The title of the video is "Object Detection Web Application with Flask and YOLOv9", but you're using YOLOv8. Please fix the title.

You are very good at what you do… trust me! After deploying it on android. How can I share the application or how someone else could get it

thank you can't wait to see you working on it