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SILICON VISION
Приєднався 16 вер 2012
I have created this channel for the learner whose are interested on Artificial Intelligence, Machine learning & Deep learning algorithms and specially on Computer vision basic to advance algorithm. Also, I will make tutorial on how we can develop AI/Computer Vision application using Kivy, Kivymd and Firebase & AWS database on cloud.
Technology I will use to make tutorial.
1. Python
2. OpenCV
3. Machine Learning
4. TensorFlow
5. Keras
6. Scikit-Learn
7. YOLO
8. Pandas
9. Matplotlib
10. Deep Learning
11. Numpy
12. Matplotlib
13. Supervision
14. Kivy and KivyMD
15. Firebase Database
By watching my tutorial you will also be learn Skills: Python , Computer Vision, OpenCV, Object Detection , Object Tracking, Object Segmentation, Image Processing, Image Analysis, Image Annotation, Face Detection and Tracking, Custom object detection and tracking, Latest Computer vision algorithm like SAM, Grounding DINO, Stable Diffusion with ControlNet etc.
Technology I will use to make tutorial.
1. Python
2. OpenCV
3. Machine Learning
4. TensorFlow
5. Keras
6. Scikit-Learn
7. YOLO
8. Pandas
9. Matplotlib
10. Deep Learning
11. Numpy
12. Matplotlib
13. Supervision
14. Kivy and KivyMD
15. Firebase Database
By watching my tutorial you will also be learn Skills: Python , Computer Vision, OpenCV, Object Detection , Object Tracking, Object Segmentation, Image Processing, Image Analysis, Image Annotation, Face Detection and Tracking, Custom object detection and tracking, Latest Computer vision algorithm like SAM, Grounding DINO, Stable Diffusion with ControlNet etc.
Car License Plate Recognition using EasyOCR | Optical Character Recognition | Easyocr
car number plate detection using easyocr model,
car license plate recognition
Optical character recognition,
Easyocr model,
best ocr model,
automatic car number plate detection ,
opencv,
python,
car license plate recognition
Optical character recognition,
Easyocr model,
best ocr model,
automatic car number plate detection ,
opencv,
python,
Переглядів: 354
Відео
OCR Model Comparison | Tesseract OCR, EasyOCR, Keras-OCR, Paddle OCR, MMOCR, OCR-SAM
Переглядів 6 тис.11 місяців тому
OCR Model Comparison: Tesseract OCR, EasyOCR, Keras-OCR, Paddle OCR, MMOCR, OCR-SAM Purpose of OCR Model: Text extraction Document digitization Data entry automation Searchability Accessibility Translation services Text analysis Forms processing Practical use case of OCR: Car Number Plate Recognition (ANPR) Receipt and Invoice Processing Document Scanning and Archiving Passport and ID Card Scan...
Opencv Autocomplete not Working on Pycharm | PyCharm cannot find cv2 references |
Переглядів 439Рік тому
#opencv #computervision #artificialintelligence #deeplearning #pycharm Solution of the error: opencv autocomplete not working on pycharm Autocomplete for OpenCV-Python in Windows not working cv2 no suggestions PyCharm cannot find cv2 references
Segment Anything Model (SAM) with Grounding DINO to detect and extract object from the image
Переглядів 2,8 тис.Рік тому
#computervision #opencv #artificialintelligence #deeplearning #machinelearning Segment Anything Model (SAM) with Grounding DINO to detect and extract object from the image according to text prompt or classes of object. Colab Notebook Link: colab.research.google.com/drive/14OD5NbTd3470WdkF_075pI4titJNab7X?usp=sharing
Image Inpainting with Segment Anything Model (SAM) and Stable Diffusion
Переглядів 2 тис.Рік тому
#computervision #opencv #artificialintelligence #deeplearning #machinelearning Image Impainting using Segment anything model and Stable Diffusion explanation with example. Here you will get details code and implementation details in Google Colab Notebook SAM Github link for instalation: !pip install 'git github.com/facebookresearch/segment-anything.git' SAM weights download link: !wget -q dl.fb...
Segment Anything Model in Python| SAM | A to Z | Segment Anything Model (SAM)
Переглядів 2,9 тис.Рік тому
#computervision #opencv #artificialintelligence #deeplearning #machinelearning Segment anything model explanation with example. Here you will get details code for extract any segmented part of image using SAM model. Image Segmentation, Image masking, Object Detection. SAM Github link for instalation: !pip install 'git github.com/facebookresearch/segment-anything.git' SAM weights download link: ...
MMOCR-Optical Character Recognition | Modular Architecture of MMOCR
Переглядів 2,3 тис.Рік тому
#computervision #deeplearning #artificialintelligence #opencv #machinelearning MMOCR is an open-source toolbox based on PyTorch and mmdetection for…… Text detection Text recognition Key information extraction It is popular for scene or curve text detection & recognition. STEPS & LINK: step 1. Clone MMOCR git clone github.com/open-mmlab/mmocr.git cd mmocr mim install -e . step 2. Then MMOCR dire...
OCR-SAM | Optical Character Recognition (OCR) with Segment Anything Model (SAM)
Переглядів 1,4 тис.Рік тому
#artificialintelligence #computervision #deeplearning #opencv #machinelearning Basically, SAM can be applied on OCR model. OCR-SAM is the combination of off-the-self OCR Model MMOCR and SAM which can put mask on detected text and several application can develop using OCR-SAM like….. Segment text from image Text removal from image and Text inpainting Step 1: git clone github.com/yeungchenwa/OCR-...
Grounding DINO | Detect Anything | No Training | Zero Shot Object Detection
Переглядів 3,1 тис.Рік тому
#computervision #artificialintelligence #deeplearning #opencv #machinelearning Grounding DINO is a self supervised zero shot object detection algorithm which can detect object from an image based on the text prompt. I have implemented it on PyCharm. Necessary command need to execute in PyCharm terminal: Clone the GroundingDINO repository from GitHub. git clone github.com/IDEA-Research/Grounding...
Grounding DINO | AssertionError: Torch not compiled with CUDA enabled | Solve Easily
Переглядів 6 тис.Рік тому
#computervision #artificialintelligence #deeplearning #opencv #machinelearning Grounding DINO: AssertionError: Torch not compiled with CUDA enabled This video will solve the AssertionError: "Torch not compiled with CUDA enabled" when you run Grounding DINO algorithm for object detection.
i want to train model on custom data how can i do that ?
4:13 media offline
paddler or mmocr which is most accurate?
Could you provide the code? The GitHub link is not working.
This solution is same as commenting the whole code to avoid error instead of finding the bug.
This solution is same as commenting the whole code to avoid errors instead of finding a bug.
Sir, Would you mind giiving me this solution of this porblem . when I want to run your code in terms of solar panel image then it is creating this problem like "SupervisionWarnings: green is deprecated: `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.GREEN` instead. SupervisionWarnings: annotate is deprecated: `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead" . Original code of your is "import supervision as sv import numpy as np mask_annotator = sv.MaskAnnotator() box_annotator = sv.BoxAnnotator(color=sv.Color.green()) detections=sv.Detections.from_sam(result) annotated_image = box_annotator.annotate(scene=image_bgr.copy(),detections=detections,skip_label=True) annotated_image = mask_annotator.annotate(scene=annotated_image.copy(),detections=detections) sv.plot_images_grid( images=[image_bgr,annotaed_image], grid_size=(1,2), titles=['Original_image','Annotated_image'], )"
@siliconvision does the notebook still work?
How to run with gpu instead of cpu?
How can we perform it on whole dataset with multi class classification? Do you have any notebook for it?
I can't find the same path 😢
doesthis work for text too as in copy original signature to paste in a document?
Hi, I tried to implement on Raspberry Pi but has the "too many value to unpack" error. Any advice? I have trimmed from 5 parameters to 4, and change the first param to "boxes.
Can share the python codes?
I have been looking for something this. Excellent quality. Loved that you also talked about multi-lingual support! Thank you! 🙂
Hi, I tryed to do the same method but I get a very incorrectly colored generated images, then i tryed SDXL instead of SD v2 and now I get black generated images, i would appreciate if you know how to solve this.
do we need to set the cuda_home first?
Its great! I am recently working on character, face, object recognition. Suggest the best libraries for: 1. Face (I use Opencv LBPH..) 2. Image to text? 3. Table Image to csv 4. Objects recognition? You should make more similar videos, soon you will be monetized.
Great suggestion! Thanks.
Could you please provide source code
could you please provide this colab notebook
colab is = a stable diffision????? I didn know that
Thanks i really wanted this 😊
Welcome 🤗
👏👏👏
Wow nice video!
Thanks!
Incredibly high-quality overview! Thank you!
You are most welcome
Nice and easy way of programming. Thanks for showing all the steps!
You are most welcome
do you freelance ? i need to segment solar panels from UAV images. If yes, can you share your email id ?
Sorry for late response. Yes, akazad.engr@gmail.com
thank you
You're welcome
thank you
You're welcome
excellent 🙂🙂
Thank you! Cheers!
did not solve the problem.
Hi could you please help us with our project? We want to detect all text on screenshot of software. This is a more complex task than we tough, Tesseract and EasyOCR doesn't work. We did a lot of preprocessing (binarization, denoise, scale*3, grayscale...) and managed to go up to 90% detection on tesseract on most software. Our software is an AI that answer question based on what it saw on the screen ( It is called onistep ). This is a desktop app. This will be used by all our client on their end so we cannot use the GPU and the answer must take 5 second maximum. We think that solution like MMocr, keras OCR or paddle OCR could work but we never worked with machine learning based OCR. Do you think you can work with us on this? Do you think it is doable? Of course it would be paid work. I also sent you an email with some image preprocessed if you wanna test our image.
Plz check mail
Nice video brom, it helped me a lot.
Thanks
I think your notebook is a bit outdated because groundino module is not working. missing some installation
this doesn't solve the problem at all...... this switches Torch to run on your CPU instead of GPU, so it will be slow as hell
Can you do about face detection and face regconition?
Yes. You can write here what you need. or You can email me: akazad.engr@gmail.com
Great tutorial sir but while solving I get "size mismatch for wrapped_model.backbone.layer1.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1])." this error. how can I solve it ?
could you share google colab link for your notebook?
Hey, even I'm looking for a project that's similar to this. Can you please send me some sources or colab links
most of the information are in the video description box..
@@siliconvision you supplied the weights not the colab file. easier to follow the video if you supply the colab notebook :)
Thanks for your suggestion.@@stevenvafadar9318
that is not solving anything what about pepole who actually want to use cuda??
has you managed to solve ? i have _C error even using pytorch with cuda
Solve? or Avoid the problem.
"promo sm" 😱
If I only want to extract 1 object from an image, it doesn't work. For example if I want to detect a bear, i have to set the classes = ['bear', 'bear'] and get 2 extractions of the same bear. Also, when I print detection class_ids, it says None, None instead of 0,1. I have to manually set class_id = [1, 0]. Also, how can I download the final extracted objects instead of just plotting them?
colab.research.google.com/drive/14OD5NbTd3470WdkF_075pI4titJNab7X?usp=sharing#scrollTo=PHUNi2wX2odl
plz check details here. After plotting in colab , you can save it in your local pc.
Brother ho can i contact you
plz email me: akazad.engr@gmail.com
Brother how i can i contact you
Sure.. Plz email me: akazad.engr@gmail.com