Hey what if i am not able to find an appropriate dataset on robolflow or anywhere else,how should i fine tune then.I want to detect cricket players in a frame ,but couldn't find a datset anywhere.Annotating 4000+ images will take a lot of time
Pardon me for a novice question - how can we prepare a dataset with bounding boxes? I have 3000 leaf images and I want to prepare a dataset where the leaf is identified. Any reference would be of great help.
Login with your mail to roboflow and you saw upload your dataset -> assign images to annotate. You will annotate box or ellipse. Annotate > generate model > export dataset > select the format you want . Either download or copy the snippet
@@Roboflowi used my own custom dataset using roboflow , no i did not change anything and i used the same dataset to run a yolov5 code and it worked perfectly fine
hello! Thank you for the video. I trained the images with yolov9, processed the test data, and saved the weights. My final goal is to detect video, but the following error keeps occurring during loading weights. Is there a way to load the yolov9 model? I used a torch. Traceback (most recent call last): File "/content/yolov9/detect.py", line 232, in main(opt) File "/content/yolov9/detect.py", line 227, in main run(**vars(opt)) File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/content/yolov9/detect.py", line 68, in run model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) File "/content/yolov9/models/common.py", line 684, in __init__ model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) File "/content/yolov9/models/experimental.py", line 76, in attempt_load ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model KeyError: 'model'
very Good. well done. Your videos are really informative. Video on Yolov5 for calcification is also good. Your vides help me to solve my issues. Thanks
What happens if the process crashes or errors out during training? Can you restart at the previous epoch or continue training from the point of the crash?
Hello, thanks for uploading the video. Please, I'm unable to run my dataset in YOLO Format on Colab. Anytime I copy and paste the codes with the API key, it will not run. Kindly help with this regards. Thanks in anticipation.
I am currently working on a project that involves tracking a specific person in a CCTV video and performing video summarization based on that person's movements. I plan to use YOLOv8 for object detection and either Deep SORT or Strong SORT for tracking. My main question is whether it is possible to track a specific person in the video using their assigned ID. For example, if I want to track the person with ID 2 in the video, can I do so without having pre-annotations or IDs for that person? Essentially, I would input a random video, obtain IDs for each person in the video, and then specify the ID of the person I want to track. I would greatly appreciate it if you could provide me with a detailed explanation or, if possible, create a video on this topic. Your insights would be invaluable to me and others working on similar projects. Can you please make a video on object detection and then video summarization on the detected object Thank you for your time and consideration. I look forward to your response.
let us consider both the cases.. but mostly the person coming in and out of the frame@@Roboflow and can you please do a video on object detection and video summarization on that detected object. thanks for your response
I'm actually working on detection of bad wrapped Maggi tablets on the moving conveyor but I'm unable to run my copied dataset on roboflow to Google Colab. Please help me on this. Thank you
it's possible do this trainning offline, just using Python? I'm new on this wolrd... and I'm don't know Colab or any Workbook technologie. I just want to test a bit on my local PC, do you have some kind of Repo for Offline mode? I mean no online trainning!
Very good explanation. One doubt on real time detection. what should be the maximum distance for YOLOv9 to detect the object? can it detect at 30ft or 40 feet could you please provide these details?
After training my model on one class, I observed good performance on the validation and test datasets. However, upon inspecting the confusion matrix generated during training, I observed a high rate of false positives with the background class. ``` Actual Class | Car | Background | Predicted Class |---------------------------------| Car | 1.0 | 1.0 | Background | | | ``` In the dataset, I provided an empty label.txt file when no object was in the image. Can you help me to understand why?
Great video. I did train my data set ,every thing worked fine except the val/losses stuck to zero during 100 training epochs .Any suggestion to solve this problem?
Thanks for the tutorial!! When using roboflow and the yolov9-c or yolov9-e model I get an AttributeError: 'list' object has no attribute 'view' in the loss_tal.py file line 168-169: File "/content/yolov9/utils/loss_tal.py", line 168, in pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split() How can this issue be solved?
@@Roboflow so you can use polygons to highlight objects of interest when creating the masks (in train, validate, test data) for YOLOv9 to detect objects?
Hello, there is a tutorial on the Ultralytics page on how to use Yolov9. You can import the Ultralytics package and start training a model with just a few lines of code (like Yolov8). What is the difference between the approach in this video and using the Ultralytics package? What are the advantages and disadvantages? Thanks for your help.
@@Roboflow - I did not change anything. I used the same command-line argument to train. Even the dataset was exported from Roboflow. Here's a traceback: Traceback (most recent call last): File "/yolov9/train.py", line 634, in main(opt) File "/yolov9/train.py", line 528, in main train(opt.hyp, opt, device, callbacks) File "/yolov9/train.py", line 277, in train for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- File "/opt/conda/lib/python3.10/site-packages/tqdm/std.py", line 1178, in __iter__ for obj in iterable: File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__ data = self._next_data() File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1346, in _next_data return self._process_data(data) File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1372, in _process_data data.reraise() File "/opt/conda/lib/python3.10/site-packages/torch/_utils.py", line 722, in reraise raise exception IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/yolov9/utils/dataloaders.py", line 656, in __getitem__ img, labels = self.load_mosaic(index) File "/yolov9/utils/dataloaders.py", line 791, in load_mosaic img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) File "/yolov9/utils/augmentations.py", line 248, in copy_paste l, box, s = labels[j], boxes[j], segments[j] IndexError: list index out of range
File "/content/yolov9/train.py", line 634, in main(opt) File "/content/yolov9/train.py", line 528, in main train(opt.hyp, opt, device, callbacks) File "/content/yolov9/train.py", line 277, in train for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- File "/usr/local/lib/python3.10/dist-packages/tqdm/std.py", line 1181, in __iter__ for obj in iterable: File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1345, in _next_data return self._process_data(data) File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1371, in _process_data data.reraise() File "/usr/local/lib/python3.10/dist-packages/torch/_utils.py", line 694, in reraise raise exception IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/content/yolov9/utils/dataloaders.py", line 656, in __getitem__ img, labels = self.load_mosaic(index) File "/content/yolov9/utils/dataloaders.py", line 791, in load_mosaic img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) File "/content/yolov9/utils/augmentations.py", line 248, in copy_paste l, box, s = labels[j], boxes[j], segments[j] IndexError: list index out of range@@Roboflow
It is GPL 3.0 so I’m afraid not. Take a look here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-dL9B9VUHkgQ.htmlsi=BrjmBB-fKo7R5EtE I listed few models with permissive licenses here. Not to advertise or anything, but you can also train YOLO-NAS on Roboflow and use it commercially. Even on free tier.
Can you upload a video on what you typically do to increase the accuracy of predictions such as hyperparameter tuning or working on the dataset? Are there any indicators from the results that could tell us what we can do to improve model accuracy or precision.
In this video you talk about too many epochs is possible in training causing over-fitting..etc, but in Yolo v8 there is protection against this with 'EarlyStopping', for example I can set up 1000 epochs and the training will stop after say 573 epochs and observe the best results and create a model as best.pt for example. Is this same feature in Yolo v9? Many thanks.
Your intro video shows specific player numbers, but I can't see that on the video (just generic players with inference certainty). Is it possible to use Yolo 9 for specific player tracking (like the splash image of this video), or is there a way Bytetrack or other plug-in can work with Yolo 9 to allow instance tracking?
Thank you for the informative video. I have a query related to my ongoing mobile app project, which involves capturing images with the camera, performing immediate cropping, and subsequently analyzing the colors using a dedicated algorithm. Can YOLOv9 facilitate real-time cropping of the captured images and allow seamless integration with code for color identification?
@@Roboflow I need to crop a photo of a cuvette, isolating only the liquid part. From there, I'll extract its color and integrate it into a pre-existing formula.
@@Roboflow That's the thing, im lost and sure how to proceed, this is why i need your help. I just need to find a way how to crop the liquid from the cuvette so i can analyze it later on (i might have not been very clear first)
@@cagataydemirbas7259 as far as I know YOLOv9 only look at boxes during training. YOLOv8 on the other hand use polygons to make detection model better.
