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this video helped me a lot. thank you Aarohi

Why do you think the results from your test were so poor? Did you just need more training images?

while executing make -j4 i am getting error: desktop:/home/nvidia/opencv/build# make -j4 make; *** No targets specified and no makefile found. Stop. How to resolve this, PLeeeessee help 🙏🙏

While I was trying to train the yolov8 model, I get an error: OSError: [Errno 12] Cannot Allocate Memory. Please help me resolve this. I'm training the yolov8 model on VS Code.

while working in the server, not able to display the video. Can you make a video how can we do in the server.

Mam where can i find this room dataset

Hi mam i need this code for practical implementation.... Nice video ❤

Please tell me how to connect a Q10F User Manual camera On Jetson nano because I tried and did not succeed, knowing that I used a video capture cable and it shows me /dev/video0 after executing ls /dev/video* knowing that my camera is video capture USB

Labels are empty in the dataset you have provided..?

Kindly post a video for Deit

Hats off to your commendable efforts

Keep up this good work

👌👌

Very nicely explained

👏👏👏

Nice

Amazing

Very well explained

Awesome

Nice

Hello Mam, i got this error how solve this error #NameError: name 'image_processor' is not defined

Thanks, do you have detailed video on video object detection with label and confidence score...or any other resource that can be helpful. Thank you.

Can you help me with a rural mountain community with problematic speeders on mountain roads?

how did you come up with the values: [0.485, 0.456, 0.406] and [0.229, 0.224, 0.225] ?

Whis is netter Yolov-9 or Florence-2

Thank you mam for teaching us sooo nicely.. I totally agree with @shahidulislamzahid... mam you are too good.

Thank you mam

🎯 Key points for quick navigation: 00:05 *📚 Florence-2 is a lightweight vision language model that can handle various tasks based on simple instructions.* 00:26 *💡 The key innovation of Florence-2 is its ability to handle tasks like object detection, captioning, and detailed image analysis using a unified approach.* 04:13 *🔍 In computer vision, models need to understand both global concepts and finer details to be effective across different tasks.* 04:54 *📍 Spatial hierarchy refers to the understanding of visual information at different scales or levels of detail within an image.* 06:03 *🔎 Semantic granularity refers to how much detail we can understand from visual information, ranging from general ideas to specific details.* 09:11 *🤝 Multitask learning involves teaching a model to do multiple related tasks at the same time to improve its overall understanding and performance.* 10:08 *💪 Universal representation learning means training a single model that can understand different types of information without processing has several phases for ensuring correct and complete annotations.* 20:39 *👀 The detailed annotation process ensures that the FLD 5B data set is properly labeled across different levels of granularity, enhancing its utility for advanced AI applications.* Made with HARPA AI

python version?

Can we install i Jetpack sdk 5 in jetson nano

Nicely explained mam😍😍😍😍

But how did the hackers manage to change your GMail password even though you have 2 factor authentication? Did they get your browser session cookies somehow ?

I was waiting for this video! Thank you!

Nice

Thankyou

great explanation!

AI renewed so soon!

amazing explanation as usual

Hi, Aarohi your videos are really helpful. I am facing the following error whenever I use model.fit function. model.fit(train_x, train_y, epochs = 6, batch_size = 10) Could you plz help why I am having this error and how to remove it? Epoch 1/6 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-98-4a0e0019ca96> in <cell line: 1>() ----> 1 model.fit(train_x, train_y, epochs = 6, batch_size = 10) 1 frames /usr/local/lib/python3.10/dist-packages/keras/src/backend.py in categorical_crossentropy(target, output, from_logits, axis) 5569 5570 """ -> 5571 target = tf.convert_to_tensor(target) 5572 output = tf.convert_to_tensor(output) 5573 target.shape.assert_is_compatible_with(output.shape) ValueError: Attempt to convert a value (SparseTensor(indices=tf.Tensor( [[0 0] [1 1] [2 1] [3 2] [4 1] [5 1] [6 2] [7 0] [8 1] [9 2]], shape=(10, 2), dtype=int64), values=tf.Tensor([1. 1. 1. 1. 1. 1. 1. 1. 1. 1.], shape=(10,), dtype=float32), dense_shape=tf.Tensor([10 3], shape=(2,), dtype=int64))) with an unsupported type (<class 'tensorflow.python.framework.sparse_tensor.SparseTensor'>) to a Tensor.

Mam. Freezing all layers except the final classification layer is called transfer learning. And customized with our own dataset so it's also fine tuned model. The way I understand is correct ?

hello ma'am , I couldn't find the c3d.pickle file , kindly help

Hello Mam can you make a video related to the : ## GCN-FFNN: A two-stream deep model for learning solution to partial differential equations

The github code does not match the one shown in the video. No: step 2.... line in the code

Ma'am, you have edited the yolov9.yaml file in models/detect/yolov9.yaml, but the pretrained model you are using is different, specifically "yolov9-e.pt." I may be wrong, but I think it is the "yolov9-e.yaml" file that should be edited instead. Btw, thanks for this informative tutorial.

😍😍😍😍😍😍😍😍 very well explained mam

😍😍😍😍

So much helpful. Waiting for the second part

I have a metal storage tank that is part of a water heater. During production of tank, leaking happen so early detection using either image processing or computer vision would be great to drop that storage tank and never let it through. I tried thermal image processing and contrast lighting but it's very difficult to see the leaking due to the water spraying and the coating around it that makes it difficult to find potential leaking holes. The video shows employee spraying water onto a pressured storage tank. Even when employee detect leaking while spraying, nothing can be detected from camera still. Any help with that please? Not sure if this one or another could work?

great video,but why the fps so slow?

I always have problem with OCR... I tried 3 different methods... Every time the region of number plate will be properly detected but then when I apply OCR on the cropped images... It just extracts #&((":! +3'srhed'hjhdv₹₹+:"+ This has given me better results than others but still OCR is detecting only about 50 percent characters correct... Idk what might be the issue