@@mrbot4onehello I am working on project like it will detect the car model in real time but I search a lot I didn't find how to train model and test it can you please suggest me it will be big help
yes they do not. But we are still waiting for the method to work on the annotated images. When we annotate the image from Autodistill then we have no method to change or update the wrong annotations . Please provide a solution for that. Thank You.@@Roboflow
Hi and Thank you for this video, for me it’s so informative at my (very low) level. Also, GO SPURS! Question for Peter: In this code, “milk bottle” is what? Is it words that will be AI translated into a bounding box? Then “bottle” is what? … the instantiating and assignment to a class? ontology=CaptionOntology({"milk bottle": "bottle", "blue cap": "cap"}). Asked another way, if I had a bunch of images of my dog, could I say =CaptionOntology({“dog”:”My_Dog_Trudy”})? Would that lead to a model that had a decent chance of identifying her with a bounding box in video of her at a dog park?
Great Video! Just wonder after deploying to Roboflow, how to verify the roboflow evaluation metrics like mAP, Precision and Recall with the one in google colab?
Hey Roboflow team. We are still waiting for the method to work on the annotated images. When we annotate the image from Autodistill then we have no method to change or update the wrong annotations . Please provide a solution for that. Thank You.
looks promising! so if I understand correctly - there are two phases: (1) auto-label an image dataset, something that you already showed in the G-SAM previous video (2) split the dataset to test/train/val and build a CV model with YOLO-8 q1: since auto-labeling is not 100% fullproof, what about the human in the loop? q2: what if the base model doesn't recognize the labels (name of specific boats I have in a marine dataset), how can I fine-tune it? so the model will find a boat, but will also "know" the brand of the boat
Overall yes. But it doesn’t need to be G-SAM and YOLOv8. You could use other base and target models. You could upload your auto labeled datasets to Roboflow for human refinement and download it back to your environment for second stage. If the base model won’t detect objects you want to find you should try some prompt engineering. But there are definitely types of objects that we won’t be able to auto annotate. Good thing is that with every base model autodistill will become more powerful.
hi, thank you for the helpful information. truly appreciate it. i have a question, is there any way for the autodistill to only return the rectangle bounding box value and not a polygon?
After executing the code, the 'confidence.txt' file is not being created in the specified output folder (output_folder). I have checked the directory structure, file extensions, and paths, but the problem persists. Any assistance in identifying the issue would be appreciated. after using this part of code im facing the following problem: from autodistill_grounded_sam import GroundedSAM base_model = GroundedSAM(ontology=ontology) dataset = base_model.label( input_folder=IMAGE_DIR_PATH, extension=".png", output_folder=DATASET_DIR_PATH) FileNotFoundError: [Errno 2] No such file or directory: '/content/dataset/annotations/confidence-3_result.txt'
Hi everyone, you're great! Just one question, is it possible to use this "technique" to create custom models, or for the recognition of particular objects not present in the basic ones recognized by yolo? Thanks so much in advance for your reply and help!
Combine this with yolo as-one and it’s pretty easy to see how different parts of a larger process can be combined into an overall framework. Could be a pipeline framework, could be something else. Probably already being worked on if not released
hi , i need a help , i need to detect shape like circle ,triangle ,rectangle in image and check all shaope are correctly drown are not , and guide me I try all shape detection model but unable tom find solution ,
Thank you for sharing. I have question about results of YOLOv8 model , after the training of the model it results in a 3-dimensional confusion matrix taking the background as a class knowing that I have a binary classification my project is classification of preforms whether it is defect or not. What can be the raisaon of appearance of this class "backgroud" and how I can solve it ? if you help me I would be grateful.
Nice Video Big Fan of your videos can you make another on Football and continue from where you left like team segmentation and count the number of passes and shoots. It would be a big help
Great video ! It helped me annotate my dataset from scratch Question : does the G-SAM annotation make a big difference compared to annotation with G-DINO ( without segmentation) ?
hello I am working on project like it will detect the car model in real time but I search a lot I didn't find how to train model and test it can you please suggest me it will be big help
how can i finetune the grounding sam model in this setup? i got some quite good results on my first attempts - but i think it can do better! thanks for the awesome tutorial!
You can’t fine tune G-SAM at least not without multiple GPU setup. But if you got good enough result with your first try. I thing best idea is to use your result YOLOv8 model to auto annotate more data and use it for training.
If I want to train a model, to detect and classify very specific objects, of which the foundation model doesn't have any labels (like on a highway senario, I don't only want to detect vehicles, like in a category car, but I also want to tell the brand of the car, or even more specific the model of the car.) would it still work? Can we teach a model with that complex classes without annotation?
I am working on project to detect a car model in real time i train 5 images but i didn't know whether they are trained well or not if they are than how to keep that data and detect car model in real time i so confuse how does this happen when i install yolo v8 it already has some in-built data detection in realtime than how can i put my own custom data in that classes
How precise is the annotation? Like...for example if I want to label 3 types of insects...does it ricognize the type or will i have just a general 'insect' label?
Hi 👋🏻 It is Peter from the video. You should always try and test, but my assumption is that it won’t be able to distinguish between different types of insects. They will semantically be to close to each others but insect class sound very reasonable.
For each insect, can't you take a video, and then adjust the CaptionOntology? I guess this would create 3 models, but maybe they could be combined or something?
Hello Hero,,, I hope you are well Is Grounded_Segmeny_Anything working on a night mode or just images within day mode Finally thanks for sharing your knowledge with us
I tried it with detecting people on night vision footage and it works. Best idea is to try and test :) I’m actually super curious if it will work for you.
Does this framework support anything? Like galaxy object detection? For example, I have Galaxy images(about 300k) and the objects visible in the image are stars and different types of galaxies. My objective is to classify galaxies into two class. Is it possible to auto annotate?
Only if they are in caption ontology. only those models if they relate to the base model. But if you do the labeling of your images and use as a base model for others it will be ground breaking
@@Roboflow if the process is very long and I need to stop in the middle to continue later, is there a way to do that or it is an atomic only operation?
hello I am working on project like it will detect the car model in real time but I search a lot I didn't find how to train model and test it can you please suggest me it will be big help
