- 39
- 1 396 820
TheCodingBug
Pakistan
Приєднався 11 січ 2020
I work as Principal Data Scientist, pioneering in Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing. I have a Ph.D. in Data Science and I am on a mission to spread data literacy.
On this channel, you will find WEEKLY hands-on video tutorials on Data Science, Computer Vision, Deep Learning, and Machine Learning. So SUBSCRIBE to the channel so that you do not miss new videos.
I aim to keep the tutorials as short and informative as possible. The videos would cover both the theory and the hands-on using Python. At the end of each tutorial, you will not only learn theoretical concepts, but also how to code them and convert into a project!
If you find my videos useful, I would love your support on Patreon: www.patreon.com/TheCodingBug
On this channel, you will find WEEKLY hands-on video tutorials on Data Science, Computer Vision, Deep Learning, and Machine Learning. So SUBSCRIBE to the channel so that you do not miss new videos.
I aim to keep the tutorials as short and informative as possible. The videos would cover both the theory and the hands-on using Python. At the end of each tutorial, you will not only learn theoretical concepts, but also how to code them and convert into a project!
If you find my videos useful, I would love your support on Patreon: www.patreon.com/TheCodingBug
YOLOv11 Instance Segmentation on Custom Dataset | Step-by-Step Guide
An in-depth Yolo v11 instance segmentation on custom dataset tutorial with a step-by-step guide, including setting up a GPU-based training environment, developing a custom instance segmentation dataset with two classes, training the model, and running inferencing on images, videos, and webcam.
** Code is available for our Patreon Supporters**
www.patreon.com/TheCodingBug
---------------------------------------------
► Time Stamps:
Introduction: (0:00)
Setting up Virtual Environment: (0:24)
Annotating custom instance segmentation dataset in YOLO format: (2:27)
Train YOLO v11 custom instance segmentation model: (7:02)
Run custom instance segmentation on images: (9:13)
Run custom instance segmentation videos: (10:48)
Run custom instance segmentation webcam: (11:40)
Export YOLOv11 custom instance segmentation to ONNX or TFLITE: (11:56)
YOLOv11 Custom Instance Segmentation Command Line: (12:30)
---------------------------------------------
📌 Links & Resources:
docs.ultralytics.com/modes/export/
docs.ultralytics.com/models/
pytorch.org/get-started/locally/
---------------------------------------------
Want to discuss more?
► Connect on Linkedin: www.linkedin.com/in/haroon-shakeel/
#TheCodingBug
---------------------------------------------
► My Other Tutorials:
○ YOLO v11 Custom Object Detection Tutorial (Win & Linux): ua-cam.com/video/A1V8yYlGEkI/v-deo.html
○ YOLO v8 Custom Instance Segmentation (Win & Linux): ua-cam.com/video/DMRlOWfRBKU/v-deo.html
○ YOLO v8 Custom Object Detection Tutorial (Win & Linux): ua-cam.com/video/gRAyOPjQ9_s/v-deo.html
○ YOLO v8 Complete Tutorial (Win & Linux): ua-cam.com/video/75LI9MI9eEo/v-deo.html
○ YOLOv7 Instance Segmentation (Win & Linux): ua-cam.com/video/tq0GI4FahWU/v-deo.html
○ YOLOv7 Pose Estimation (Win & Linux): ua-cam.com/video/z1UN7TbcRgM/v-deo.html
○ YOLOv7 Custom Object Detection (Colab): ua-cam.com/video/_fXABNYlZhY/v-deo.html
○ YOLOv7 Custom Object Detection (Win & Linux): ua-cam.com/video/-QWxJ0j9EY8/v-deo.html
○ YOLOv7 Complete Tutorial (Colab): ua-cam.com/video/_CkXDjmT8dc/v-deo.html
○ YOLOv7 Complete Tutorial (Windows and Linux): ua-cam.com/video/n2mupnfIuFY/v-deo.html
○ 40 Object Detection Models in TensorFlow: ua-cam.com/video/2yQqg_mXuPQ/v-deo.html
○ Realtime Object Detection on CPU with OpenCV: ua-cam.com/video/hVavSe60M3g/v-deo.html
○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 2 (Training Models): ua-cam.com/video/GoItxr16ae8/v-deo.html
○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 1 (Preparing Data): ua-cam.com/video/ffTURA0JM1Q/v-deo.html
○ Detectron2 on Colab: ua-cam.com/video/bjay7fT934Y/v-deo.html
○ Instance Segmentation as Rendering: ua-cam.com/video/gnXlE9BO0lo/v-deo.html
○ Detectron2 Complete Tutorial: ua-cam.com/video/Pb3opEFP94U/v-deo.html
○ Colorize Black and White Images and Videos using Python OpenCV: ua-cam.com/video/EZWHAd0IH1M/v-deo.html
○ Face Detection Using OpenCV Python with CUDA GPU Acceleration: ua-cam.com/video/GXcy7Di1oys/v-deo.html
○ Build and Install OpenCV 4.5.1 With CUDA GPU Support on Windows 10: ua-cam.com/video/YsmhKar8oOc/v-deo.html
○ Install TensorFlow Under 90 Seconds
ua-cam.com/video/toJe8ZbFhEc/v-deo.html
○ Install PyTorch Under 90 Seconds
ua-cam.com/video/raBkhUoeOHs/v-deo.html
---------------------------------------------
► Follow us on Twitter: BugCodingThe
► Support us on Patreon: www.patreon.com/TheCodingBug
---------------------------------------------
DISCLAIMER: Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. There is no additional charge to you!
** Code is available for our Patreon Supporters**
www.patreon.com/TheCodingBug
---------------------------------------------
► Time Stamps:
Introduction: (0:00)
Setting up Virtual Environment: (0:24)
Annotating custom instance segmentation dataset in YOLO format: (2:27)
Train YOLO v11 custom instance segmentation model: (7:02)
Run custom instance segmentation on images: (9:13)
Run custom instance segmentation videos: (10:48)
Run custom instance segmentation webcam: (11:40)
Export YOLOv11 custom instance segmentation to ONNX or TFLITE: (11:56)
YOLOv11 Custom Instance Segmentation Command Line: (12:30)
---------------------------------------------
📌 Links & Resources:
docs.ultralytics.com/modes/export/
docs.ultralytics.com/models/
pytorch.org/get-started/locally/
---------------------------------------------
Want to discuss more?
► Connect on Linkedin: www.linkedin.com/in/haroon-shakeel/
#TheCodingBug
---------------------------------------------
► My Other Tutorials:
○ YOLO v11 Custom Object Detection Tutorial (Win & Linux): ua-cam.com/video/A1V8yYlGEkI/v-deo.html
○ YOLO v8 Custom Instance Segmentation (Win & Linux): ua-cam.com/video/DMRlOWfRBKU/v-deo.html
○ YOLO v8 Custom Object Detection Tutorial (Win & Linux): ua-cam.com/video/gRAyOPjQ9_s/v-deo.html
○ YOLO v8 Complete Tutorial (Win & Linux): ua-cam.com/video/75LI9MI9eEo/v-deo.html
○ YOLOv7 Instance Segmentation (Win & Linux): ua-cam.com/video/tq0GI4FahWU/v-deo.html
○ YOLOv7 Pose Estimation (Win & Linux): ua-cam.com/video/z1UN7TbcRgM/v-deo.html
○ YOLOv7 Custom Object Detection (Colab): ua-cam.com/video/_fXABNYlZhY/v-deo.html
○ YOLOv7 Custom Object Detection (Win & Linux): ua-cam.com/video/-QWxJ0j9EY8/v-deo.html
○ YOLOv7 Complete Tutorial (Colab): ua-cam.com/video/_CkXDjmT8dc/v-deo.html
○ YOLOv7 Complete Tutorial (Windows and Linux): ua-cam.com/video/n2mupnfIuFY/v-deo.html
○ 40 Object Detection Models in TensorFlow: ua-cam.com/video/2yQqg_mXuPQ/v-deo.html
○ Realtime Object Detection on CPU with OpenCV: ua-cam.com/video/hVavSe60M3g/v-deo.html
○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 2 (Training Models): ua-cam.com/video/GoItxr16ae8/v-deo.html
○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 1 (Preparing Data): ua-cam.com/video/ffTURA0JM1Q/v-deo.html
○ Detectron2 on Colab: ua-cam.com/video/bjay7fT934Y/v-deo.html
○ Instance Segmentation as Rendering: ua-cam.com/video/gnXlE9BO0lo/v-deo.html
○ Detectron2 Complete Tutorial: ua-cam.com/video/Pb3opEFP94U/v-deo.html
○ Colorize Black and White Images and Videos using Python OpenCV: ua-cam.com/video/EZWHAd0IH1M/v-deo.html
○ Face Detection Using OpenCV Python with CUDA GPU Acceleration: ua-cam.com/video/GXcy7Di1oys/v-deo.html
○ Build and Install OpenCV 4.5.1 With CUDA GPU Support on Windows 10: ua-cam.com/video/YsmhKar8oOc/v-deo.html
○ Install TensorFlow Under 90 Seconds
ua-cam.com/video/toJe8ZbFhEc/v-deo.html
○ Install PyTorch Under 90 Seconds
ua-cam.com/video/raBkhUoeOHs/v-deo.html
---------------------------------------------
► Follow us on Twitter: BugCodingThe
► Support us on Patreon: www.patreon.com/TheCodingBug
---------------------------------------------
DISCLAIMER: Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. There is no additional charge to you!
Переглядів: 3 260
Відео
YOLOv11 Object Detection on Custom Dataset | Step-by-Step Guide
Переглядів 13 тис.Місяць тому
A comprehensive YOLOv11 custom object detection tutorial with a step-by-step guide for a two-class custom dataset. I cover setting up an environment for YOLOv11, how to annotate custom datasets in YOLO format, and how to train custom object detection models. Although I show a demonstration using YOLOv11 medium version, the method is generic enough to train all variations of YOLOv11 custom objec...
YOLOv8 Object Detection and Segmentation Models Comparison (No Tutorial)
Переглядів 22 тис.Рік тому
This is a comparison of YOLOv8 object detection and segmentation models out of the box. The comparison is on GTX1060 GPU. ► YOLOv8 Tutorials: ○ YOLOv8 Custom Instance Segmentation Tutorial (Win & Linux): ua-cam.com/video/DMRlOWfRBKU/v-deo.html ○ YOLOv8 Custom Object Detection (Win & Linux): ua-cam.com/video/gRAyOPjQ9_s/v-deo.html ○ YOLOv8 Complete Tutorial (Win & Linux): ua-cam.com/video/75LI9M...
YOLOv8 Instance Segmentation on Custom Dataset | Windows & Linux
Переглядів 70 тис.Рік тому
A complete YOLOv8 custom instance segmentation tutorial that covers annotating custom dataset with polygons, converting the annotations to YOLOv8 format, training custom instance segmentation model, and inferencing on images, videos, and webcams. I show how to train YOLOv8 medium version on the custom dataset but you can use any variation of YOLOv8. Code is available for our Patreon Supporters ...
YOLOv8 Object Detection on Custom Dataset | Step by Step Tutorial
Переглядів 183 тис.Рік тому
A complete YOLOv8 custom object detection tutorial with a two-classe custom dataset. I cover how to annotate custom datasets in YOLO format, set up an environment for YOLOv8, and train custom object detection models. Although I have demonstrated using the YOLOv8 medium version, the method is generic enough to train all variations of YOLOv8. Code is available for our Patreon Supporters www.patre...
YOLOv8 COMPLETE Tutorial | Object Detection | Segmentation | Classification
Переглядів 56 тис.Рік тому
YOLOv8 object detection model is the current state-of-the-art. The model is also trained for image segmentation and image classification tasks. In this tutorial, we cover how to run pre-trained YOLOv8 on Windows/Linux for all three tasks, and we will also include how to export to other formats (such as ONNX or TFLITE). This includes running YOLOv8 on images, videos, and webcams. Code is availab...
YOLO v7 + SORT Object Tracking | Windows & Linux
Переглядів 45 тис.2 роки тому
Implement multiple object tracking in Python with YOLO v7 and SORT tracking algorithm. Code is available for our Patreon Supporters www.patreon.com/TheCodingBug ► Time Stamps: Introduction: (0:00) Installations: (0:20) YOLO v7 Object Detection: (03:00) YOLO v7 Tracking: (04:08) Tracking Specific Class: (04:25) Show Track: (05:09) Unique Color for Each Track: (05:27) Hide Labels and BBOX: (05:40...
Official YOLO v7 Instance Segmentation COMPLETE TUTORIAL | Windows & Linux
Переглядів 30 тис.2 роки тому
This YOLO v7 instance segmentation tutorial is focused on using official pre-trained YOLO v7 mask model. I cover how to set up the environment, prereqs for the YOLO v7 mask, and we code from scratch to run instance segmentation on images, videos, and webcam. Code is available for our Patreon Supporters www.patreon.com/TheCodingBug ► Time Stamps: Introduction: (0:00) Installing Prereqs: (0:20) S...
Official YOLO v7 Pose Estimation | Windows & Linux
Переглядів 20 тис.2 роки тому
This is the official YOLO v7 pose estimation tutorial built on the official code. The tutorial shows how to set up and use the pre-trained YOLO v7 model, along with modifications for removing bounding boxes and showing FPS on videos. It is worthwhile to note that the repository used in the tutorial is different from the yolo v7 object detection one. Code is available for our Patreon Supporters ...
Official YOLO v7 Custom Object Detection on Colab
Переглядів 39 тис.2 роки тому
This YOLO v7 custom object detection tutorial is focused on training the custom model on Google Colab. After training, you can run inferencing locally or on Colab. The method is generic enough to train all seven variations of official YOLO v7 models. In our previous tutorial, we trained the base YOLO v7 custom object detector, and in this tutorial, we train YOLOv7x to compare the performance of...
Official YOLO v7 Custom Object Detection Tutorial | Windows & Linux
Переглядів 122 тис.2 роки тому
This is a complete YOLO v7 custom object detection tutorial, starting from annotating the custom dataset, setting up environment for training custom model, and any modifications required in the official repository files. The method is generic enough to train all seven variations of official YOLO v7 models. Code and custom dataset is available for our Patreon Supporters www.patreon.com/TheCoding...
Official YOLO v7 Object Detection COMPLETE Tutorial for Google Colab
Переглядів 42 тис.2 роки тому
This YOLO v7 tutorial enables you to run object detection in colab. This is a complete tutorial and covers all variations of the YOLO v7 object detector. YOLO v7 has just been released and shows accuracy and speed improvements over its predecessors. Currently, YOLO v7 is the world's fastest and most accurate object detector. This tutorial is focused on running pre-trained YOLO v7 models (all se...
YOLO v7 Object Detection Models: FPS & Object Detection Comparison (All 7 Models)
Переглядів 10 тис.2 роки тому
YOLO v7 object detection model has 7 variants. This video showcases FPS and objects detection accuracy of all the models. There is a tradeoff between speed and accuracy, and this comparison is helpful in deciding the sweet spot. Which model variation works best for your case? Let me know in the comments down below. Want to discuss more? ►Join my discord: discord.gg/kUUvTQTTau #TheCodingBug ► My...
Official YOLO v7 COMPLETE Object Detection Tutorial | Windows & Linux
Переглядів 49 тис.2 роки тому
YOLO v7 object detection tutorial for Windows and Linux. This is a complete tutorial and covers all variations of the YOLO v7 object detector. YOLO v7 has just been released and exhibits a performance and speed boost over its predecessors. This tutorial is focused on running pre-trained YOLO v7 models (all seven variants). YOLO v7 surpasses all known object detectors in both speed and accuracy ...
TensorFlow Object Detection with 40 Models | Complete Step-by-Step Guide
Переглядів 86 тис.3 роки тому
Learn TensorFlow Object Detection from scratch! In this beginner-friendly tutorial, we'll dive into TensorFlow’s Object Detection API and work with 40 different models from the TensorFlow Model Zoo, including EfficientDet, SSD, and Faster R-CNN. You’ll get step-by-step guidance on installing TensorFlow, downloading models, and writing reusable code to perform object detection with any of these ...
Realtime Object Detection Using OpenCV Python ON CPU | OpenCV Object Detection Tutorial
Переглядів 48 тис.3 роки тому
Realtime Object Detection Using OpenCV Python ON CPU | OpenCV Object Detection Tutorial
DETECTRON2 Custom Object Detection, Custom Instance Segmentation: Part 2 (Training Custom Models)
Переглядів 25 тис.3 роки тому
DETECTRON2 Custom Object Detection, Custom Instance Segmentation: Part 2 (Training Custom Models)
DETECTRON2 Custom Object Detection, Custom Instance Segmentation: Part 1 (Develop Custom Dataset)
Переглядів 12 тис.3 роки тому
DETECTRON2 Custom Object Detection, Custom Instance Segmentation: Part 1 (Develop Custom Dataset)
DETECTRON2 TUTORIAL for Colab | Object Detection, Instance Segmentation on Google Colab
Переглядів 12 тис.3 роки тому
DETECTRON2 TUTORIAL for Colab | Object Detection, Instance Segmentation on Google Colab
DETECTRON2 PointRend Tutorial | Accurate Instance Segmentation via Rendering
Переглядів 6 тис.3 роки тому
DETECTRON2 PointRend Tutorial | Accurate Instance Segmentation via Rendering
COMPLETE DETECTRON2 TUTORIAL | Instance Segmentation, Object Detection, Keypoints Detection and more
Переглядів 65 тис.3 роки тому
COMPLETE DETECTRON2 TUTORIAL | Instance Segmentation, Object Detection, Keypoints Detection and more
Install PyTorch GPU on Windows 10 or Linux IN 90 SECONDS with Just One Command
Переглядів 7 тис.3 роки тому
Install PyTorch GPU on Windows 10 or Linux IN 90 SECONDS with Just One Command
Video & Image Colorization Using OpenCV Python | AI COLORIZATION FOR IMAGES & VIDEOS
Переглядів 8 тис.3 роки тому
Video & Image Colorization Using OpenCV Python | AI COLORIZATION FOR IMAGES & VIDEOS
Install TensorFlow GPU on Windows 10 IN 90 SECONDS with Just Two Commands | 2021
Переглядів 23 тис.3 роки тому
Install TensorFlow GPU on Windows 10 IN 90 SECONDS with Just Two Commands | 2021
Face Detection Using OpenCV with CUDA GPU Acceleration | Images, Videos
Переглядів 18 тис.3 роки тому
Face Detection Using OpenCV with CUDA GPU Acceleration | Images, Videos
Build and Install OpenCV With CUDA GPU Support on Windows 10 | OpenCV 4.5.1 | 2021
Переглядів 65 тис.3 роки тому
Build and Install OpenCV With CUDA GPU Support on Windows 10 | OpenCV 4.5.1 | 2021
YOLOv4 TFLite Object Detection Android App Tutorial Using YOLOv4 Tiny, YOLOv4, and YOLOv4 Custom
Переглядів 49 тис.3 роки тому
YOLOv4 TFLite Object Detection Android App Tutorial Using YOLOv4 Tiny, YOLOv4, and YOLOv4 Custom
Object Tracking with TensorFlow YOLOv4 | Linux, Windows
Переглядів 8 тис.3 роки тому
Object Tracking with TensorFlow YOLOv4 | Linux, Windows
Custom Object Detection with TensorFlow YOLOv4, and TFLite | Windows, Linux | Image, Video
Переглядів 9 тис.3 роки тому
Custom Object Detection with TensorFlow YOLOv4, and TFLite | Windows, Linux | Image, Video
YOLOv4 Custom Object Detection Tutorial: Part 2 (Training YOLOv4 Darknet on Custom Dataset)
Переглядів 29 тис.3 роки тому
YOLOv4 Custom Object Detection Tutorial: Part 2 (Training YOLOv4 Darknet on Custom Dataset)
sir how can we perform it on pycharm or visual studio code
Yo thanks 🙏
can someone please answer this. i am a just found after searching for so so long. I am wandring why he moved "6" images and "6" txt files form imges and lables for the val folder? is this a madetory step? and Why only 6? Thanks for this tutorial.
Val is for validation of the model. The images that are in train should not be in val. It doesn't have to be 6. Usually its 80:20 ratio for train and val respectively (i.e 80% of total images and their txt files in train and remaining in val).
@@TheCodingBug Thanks Very much
Bro can yo do a video how to generate graph from the trained model
How to make PCB defect detection in real time with a USB webcam, bad solder, component rotation, marking, wrong text, number, missing components, damage...
This video is a literal gold mine
Really nice and to the point. Great tutorial
Your tutorials are very helpful and informative. It would be really cool to see a video about OBB (oriented bounding boxes). Especially about precise determination of the rotation angle of an object.
@ThecodingBug successfully run everything but saved images and videos don't have any bounding boxes and confidence. Help me with this
Set half=False. It should solve the problem... Or use YOLOv11 or Yolov8.
Any chance of making a video that implements a tracker over object detection? I loved the custom model training video, but would love to learn how to implement a tracker over it!
I have created a video using yolo and incorporated tracker in it as well.
how I use this with tensorrt ? quand why some peoples use pytorch or tensorflow for this
You can export the model to tensorRT via model.export.
hello, my laptop has no GPU , can i execute these models on CPU ? please confirm
Hi, its very good tutorial, thank you for providing such a good material. I am trying it in google colab. getting the following error ---> 42 detections = self.model(inputTensor) 43 44 bboxs = detections['detection_boxes'][0].numpy() TypeError: 'str' object is not callable can you please help thank you
I trained a small dataset (53 samples) of one egg in different positions and places, but never detected any of them during prediction process
UPDATE: it's worked after adding the parameter amp=False to train function and trained the model again
Thanks for this video! When you train the custom model, does it get rid of the default object detection? What's the best way to ensure we keep the pre-trained model and just append the custom objects to it?
Yes it will not detect original classes. You'll need to meege your data annotations with oroginal coco data annotations to achieve this.
sir the torch still says false even after installing it like you showed
You should have nVidia GPU with latest driver installed. If still doesn't help, use older version of cuda when installing pytorch.
@@TheCodingBug Tysm sir, love you
command block says "your data is not for segmentation its detect data" or something like that. i guess its because labelme2yolo function returns us just txt and jpeg files. but segmentation needn json and png. also that txt file the function returned includes bounding box coordinates. its not for segmentation too. so it didnt work. you have an advice for me?
Use label studio instead. Check Yolov11 segmentation tutorial on my channel. It shows how to use it for annotations. ua-cam.com/video/3LN23XJC28U/v-deo.html
@@TheCodingBug thanks! I will try it and then im gonna write here if it is works or not
@@TheCodingBug it worked, thanks!
This Video was amazing. I like how you explain it, like I was 4years old. Thank you very much you deserve like and subscribe
Hello, I am a student majoring in computer science from China. I watched your video today and it was very good. I would like to ask if you could send the data of street.mp4 mentioned at the end of this video. Also, where do you get these pictures and video data like in the video? I had a hard time finding it online
These are royalty free videos available on multiple websites online.
@@TheCodingBug Thank you very much
Hello. I try to run this example, but i have a prolems. Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Users\domin\anaconda3\envs\yolov11_seg\Lib\multiprocessing\spawn.py", line 122, in spawn_main exitcode = _main(fd, parent_sentinel) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\domin\anaconda3\envs\yolov11_seg\Lib\multiprocessing\spawn.py", line 131, in _main prepare(preparation_data) File "C:\Users\domin\anaconda3\envs\yolov11_seg\Lib\multiprocessing\spawn.py", line 246, in prepare _fixup_main_from_path(data['init_main_from_path']) File "C:\Users\domin\anaconda3\envs\yolov11_seg\Lib\multiprocessing\spawn.py", line 297, in _fixup_main_from_path main_content = runpy.run_path(main_path,
Are you using multiprocessing?
@@TheCodingBug Thank you! I messed up, rewatched the video, and understood. Now I'll try again.
Why is my code showing 33 problems even though i write the code exactly as it is shown and even when my codes are in the same (model_data)file. Plz help
why i cant install torch? It's said that torch.cuda.is_available() False
Wow, you just showed everything about YOLO in 14 mins!!! 👍👍👍
I hope it's useful.
Again, thanks for the tutorials!
You're welcome!
👍👍
Hi, i could not see the files best.pt in weights, can you help? Inside weights were 2 zip folders: best and last. I extracted them but could not find the file best.pt anywhere. Im on linux.
How I contact you
You can contact me via LinkedIn. The link is pinned on my channel home page.
I'm on important project, can anyone help in resolving this error: 2024-10-23 12:16:59.495880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-10-23 12:16:59.515033: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-10-23 12:16:59.520830: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered YOLOR 🚀 v0.1-128-ga207844 torch 2.4.1+cu121 CUDA:0 (Tesla T4, 15102.0625MB) tensorboard: Start with 'tensorboard --logdir runs/train', view at localhost:6006/ wandb: Using wandb-core as the SDK backend. Please refer to wandb.me/wandb-core for more information. wandb: (1) Create a W&B account wandb: (2) Use an existing W&B account wandb: (3) Don't visualize my results wandb: Enter your choice:
Can i have ur images and video for training and test?
The trained model, code, images, video is available for our patreon supporters.
I got it working right away, thank you!
I am glad it worked out fine.
@@TheCodingBug I have cuda 11.8 version but it saying false Is it mandatory to install 12.1 version?
very useful for the initiatives
Thanks for the tutorials it is so good 👌, Can we make same process but using Google Colab for free GPU ?
Bro pls do video for amd🙏🙏🙏 i mean rx6700xt or others/// I know that is possible via ROCm but i dont know ho to do it:///
I will try.
@@TheCodingBug thank you!
Hello! I have some sort of a bug when I train my model. I have a 4VRAM gpu and I receive an error that I do not have enough space, even if i reduce the size of the batch and using the nano yolo version. Then i moved to cpu, having 24gb ram, and after 2/3 epochs the training stops randomly without any warning. Any idea why this happens?
I am not sure about sudden training stop. Can you share the error? About "out of cuda memory" error, reduce the btch size to 1.
@@TheCodingBug Regarding to cuda error, is there a gradient accumulation argument to use. I don't think training with a single sample per optim step is fine. The problem with sudden stopping is not yielding any kind of error or warning. The last thing i see in the console is the epoch progress and then the path of the program on the next line:). Some said it might be from lack of memory but 24gb should be more than enough for such a small yolo. Maybe i'll go on colab even if i hate it.
Thanks for the tutorials !
for multiple classes do i put them all in the same images file and the class in the class file or do i need to have different for all?
height, width = original_image.shape[:2] AttributeError: 'NoneType' object has no attribute 'shape'
Thanks for this nice tutorial, (with useful details) . With which model of Nvidia GPU you got these results ?
I used GTX1060.
can i run the predict code on visual stido code? please help me
Can you share your code or github?
The code is available for our patreon supporters.
very very good, could share the link of the video about contraction workers, used for testing the model
AMAZIIING TUTORIAAAAL !! pleaseeee, how to import that .onnx in unity? for object detection? Thaaaaanks
hi. I followed the instruction but why am I getting this error. Traceback (most recent call last): File "C:\Users\jisi\Desktop\YoloV7_Instance\yolov7-mask\segment.py", line 67, in <module> onImage() NameError: name 'onImage' is not defined tia
The method onImage have wrong spellings or you've missed something there
Damnnn You even solved the gpu part great work man
WARNING: Dataset not found, nonexistent paths: ['/content/drive/MyDrive/TheCodingBug/yolov7/content/drive/MyDrive/TheCodingBug/yolov7/data/val'] Traceback (most recent call last): File "/content/drive/MyDrive/TheCodingBug/yolov7/train.py", line 616, in <module> train(hyp, opt, device, tb_writer) File "/content/drive/MyDrive/TheCodingBug/yolov7/train.py", line 97, in train check_dataset(data_dict) # check File "/content/drive/MyDrive/TheCodingBug/yolov7/utils/general.py", line 173, in check_dataset raise Exception('Dataset not found.') Exception: Dataset not found. can anyone help me to resolve this error?
can't believe it how you managed to explain it so beautifully in such a less time and over complicating things, thx a ton man!!
I think rather than teaching you are showing how speed you are right?
Yes. But you can watch it at 0.25x speed.
Hi, i have some question, about the images, can i add my own images? like local images for example a school uniform?
Yes you can create your own dataset with your own images.
@@TheCodingBug thank you for the reply
Amazing, thank you