Excellent! How does detection speed compare to YOLOv8? If the detector is not good in a certain domain, is it possible to retrain it to improve in that domain?
The model is quite slow - 8 fps on A100. So it will not replace YOLO models in real-time scenarios. It can for sure partially automate the data annotation process. And yes it is possible to fine-tune the model on the selected dataset.
HI Bro,an error occurred:Error occurred when executing GroundingDinoModelLoader (segment anything): What should I do to deal this File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 151, in recursive_execute output_data, output_ui = get_output_data(obj, input_data_all) File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 81, in get_output_data return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 74, in map_node_over_list results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything ode.py", line 286, in main dino_model = load_groundingdino_model(model_name) File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything ode.py", line 117, in load_groundingdino_model get_local_filepath( File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything ode.py", line 111, in get_local_filepath download_url_to_file(url, destination) File "F:\Blender_ComfyUI\python_embeded\lib\site-packages\torch\hub.py", line 620, in download_url_to_file u = urlopen(req)
I was just as excited watching you work with GroundingDINO as you were working with it. Can you please use it to do real-time object detection with a live webcam?
Thanks a lot 🙏🏻 Grounding DINO is unfortunately too slow to run in real time ;/ But I have a prototype of notebook to use it for tracking. But not real time. Static files.
Amazing work. I believe pre-encoding feature of images is a good idea. In this way, replacing text-prompt can also quickly search in the pre-encoding feature, which will have outstanding improvements in large-scale dataset automatic annotation and other work. Do developers have such plans?
Hi, thanks i corrected my problem by identifying the actual data. sir how can do this GroundingDINO usng count and live cam same video wity Track & Count Objects. i dont know the syntax for GroundingDINO
Great video! My brain melted a little when I saw it detecting classes it was not trained on. Is the data all local, or does the inference use an API to reach out to the internet somewhere to get results? If it's all local, what is the total size cost? That is, instead of a pre-trained 40MB yolov8 model we now have... 400GB of something else?
Good question. No the model works 100 local. You just need to download the weights file + (I guess) some additional weights for backbone and you are good to go. From that point you can shut down the internet. As for the size. I’m not sure how much space it takes on the machine but it fits on single GPU. So it can’t be anything crazy. (You can check that by running nvidia-smi command once again after you load the model. I’m AFK… co I can’t do it myself now.) But I remember that the static weights file is around 200MB.
Amazing work thank you I trained YOLO V8 instance segmentation model and I 'm using it to predict Fish Species now I would like to predict fish freshness using zero shot learning can I get some help. thank you in advance.
Best thing is to start with our notebook, upload some images of fish you want to detect and write some test queries. Just to get some idea how it performs.
Hi ! I truly enjoyed yout video !! I am trying to use Grounding Dino (demo file) and Grounded Sam but I would like to use it without internet connexion ( the demo try to download bert base model from hugging face) and I would like to know how can deal with it ? I have tried to download the json file and to clone the bert model but I am really lost concerning what to add/modify to use it like that :( Thanks in advance !!
I tried to use it to segment a document containing tabular data, it didn't work at all, I tried different prompts, including: tables, lines, border lines, segmentation I also tried to explain what is that document it keeps returning the entire document
If I understood correctly, it's still better for real-time object detections to use yolov8 for example than Grounding Dino, right? Especially if only 1-2 classes are to be detected
I am working on a project where I am using grounding Dino I want to automate the process of entering classes or text-prompt manually , I just want to pass the image and grounding Dino detects everything and extract the label similar like detGpt , is it possible ? please help
It is hard to tell. Sounds like a very narrow and specific use case. I'd say take a few images that would be representable of the dataset that you expect to work with. And upload it to our Google Colab and test :)
can we classify between same object of different type , what prompts should we give then? suppose we are trying to classify "severity of burns on skin" and we want to annotate the data with 'severe', 'moderate' or 'slight' burn. can it annotate that ?
great introduction to Grounding Dino. Some questions I have: how can we train our model to recognize different states of an item e.g. : a car that is missing a wheel, or has its windshield broken ?
@@HassanGeedi-h8o you would need to write a small method to crop. We actually have open issue about that: github.com/roboflow/supervision/issues/75 we will add that soon to supervision, but you can take a look at code proposals here.
This is amazing work, well done! I was wondering what it's performance would look like for real-time crowd detection. Do you have any metrics for running inference on an embedded device such as Jetson?
Unfortunately not, but given the fact that it is only 8fps on A100 GPU I wouldn’t count on any real time applications. I work on few demos. But there are other really cool use cases that I’m exploring.
I just linked a dataset of images of wheat spikes and it's detecting them! I have a lot to learn because our work so far has been focused on the YOLO family, but this is completely fascinating and out of my scope. Thank you for sharing and please share your insights!
@@lorenzoleongutierrez7927 Hi I guess YOLO architecture is still helpful and needed. Grounding DINO is powerful but slow - only 8 fps on NVIDIA A100. But a strong zero-shot object detector like this can be used for example, to auto-annotate images. As for the theory behind the model I work on blog post right now. It should be out today/tomorrow. Could you share which dataset you used? Is it on Roboflow Universe?
When I load Grounding DINO Model this error occurs: FileNotFoundError: [Errno 2] No such file or directory: '/content/weights/groundingdino_swint_ogc.pth' Would you help me?
Excellent! How does detection speed compare to YOLOv8? If the detector is not good in a certain domain, is it possible to retrain it to improve in that domain?
The model is quite slow - 8 fps on A100. So it will not replace YOLO models in real-time scenarios. It can for sure partially automate the data annotation process. And yes it is possible to fine-tune the model on the selected dataset.
❤️
Hi , how can I finetune this Grounding Dino for object detection
HI Bro,an error occurred:Error occurred when executing GroundingDinoModelLoader (segment anything):
What should I do to deal this
File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 151, in recursive_execute
output_data, output_ui = get_output_data(obj, input_data_all)
File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 81, in get_output_data
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
File "F:\Blender_ComfyUI\ComfyUI\execution.py", line 74, in map_node_over_list
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything
ode.py", line 286, in main
dino_model = load_groundingdino_model(model_name)
File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything
ode.py", line 117, in load_groundingdino_model
get_local_filepath(
File "F:\Blender_ComfyUI\ComfyUI\custom_nodes\comfyui_segment_anything
ode.py", line 111, in get_local_filepath
download_url_to_file(url, destination)
File "F:\Blender_ComfyUI\python_embeded\lib\site-packages\torch\hub.py", line 620, in download_url_to_file
u = urlopen(req)
I was just as excited watching you work with GroundingDINO as you were working with it. Can you please use it to do real-time object detection with a live webcam?
Thanks a lot 🙏🏻 Grounding DINO is unfortunately too slow to run in real time ;/ But I have a prototype of notebook to use it for tracking. But not real time. Static files.
@@Roboflownow you can go with gdino 1.5 edge
Amazing work. I believe pre-encoding feature of images is a good idea. In this way, replacing text-prompt can also quickly search in the pre-encoding feature, which will have outstanding improvements in large-scale dataset automatic annotation and other work. Do developers have such plans?
Hi, thanks i corrected my problem by identifying the actual data. sir how can do this GroundingDINO usng count and live cam same video wity Track & Count Objects. i dont know the syntax for GroundingDINO
Hi 👋🏻 Grounding DINO is to slow for real time processing unfortunately :/
Great video! My brain melted a little when I saw it detecting classes it was not trained on. Is the data all local, or does the inference use an API to reach out to the internet somewhere to get results? If it's all local, what is the total size cost? That is, instead of a pre-trained 40MB yolov8 model we now have... 400GB of something else?
Good question. No the model works 100 local. You just need to download the weights file + (I guess) some additional weights for backbone and you are good to go. From that point you can shut down the internet. As for the size. I’m not sure how much space it takes on the machine but it fits on single GPU. So it can’t be anything crazy. (You can check that by running nvidia-smi command once again after you load the model. I’m AFK… co I can’t do it myself now.) But I remember that the static weights file is around 200MB.
amazing world of computer vision
keep us updated
you are awesome
Agree! And thanks a lot. Will do ;)
Amazing work thank you
I trained YOLO V8 instance segmentation model and I 'm using it to predict Fish Species now I would like to predict fish freshness using zero shot learning can I get some help.
thank you in advance.
Best thing is to start with our notebook, upload some images of fish you want to detect and write some test queries. Just to get some idea how it performs.
Hi ! I truly enjoyed yout video !! I am trying to use Grounding Dino (demo file) and Grounded Sam but I would like to use it without internet connexion ( the demo try to download bert base model from hugging face) and I would like to know how can deal with it ? I have tried to download the json file and to clone the bert model but I am really lost concerning what to add/modify to use it like that :(
Thanks in advance !!
I forget to specify that I am currently using linux and Pycharm to run the file demo :)
got this error
AttributeError Traceback (most recent call last)
in ()
22
23 get_ipython().run_line_magic('matplotlib', 'inline')
---> 24 sv.show_frame_in_notebook(annotated_frame, (16, 16))
AttributeError: module 'supervision' has no attribute 'show_frame_in_notebook'
That one should be fixed already
wow great. do you think Robolow will embed this feature in the GUI?
We are not yet sure. We need to explore more :) Stay tuned!
predict & predict_with_caption result is not working with your supervision box annotation... for Grounding DINO
I tried to use it to segment a document containing tabular data, it didn't work at all, I tried different prompts, including: tables, lines, border lines, segmentation
I also tried to explain what is that document
it keeps returning the entire document
Wow, pretty cool
If I understood correctly, it's still better for real-time object detections to use yolov8 for example than Grounding Dino, right? Especially if only 1-2 classes are to be detected
I am working on a project where I am using grounding Dino I want to automate the process of entering classes or text-prompt manually , I just want to pass the image and grounding Dino detects everything and extract the label similar like detGpt , is it possible ?
please help
GroundingDino is ignoring my commas and aggregating the words in the full text prompts (anything the object looks like, combined into one string).
Could you give me some examples?
GroundingDino is GERMAN?
It's such an amazing repo
Hi, nice video! I have a question If I wanted to use it to detect problems in, for example, wind turbines, would it be useful to me?
King regards!
It is hard to tell. Sounds like a very narrow and specific use case. I'd say take a few images that would be representable of the dataset that you expect to work with. And upload it to our Google Colab and test :)
@@Roboflow thank you, im gonna try to re order a 4 thousand database and i Will try to use another form
Great video! so Is it possible to apply transfer learning to our own data set?
Thanks a lot! Could you elaborate on the idea?
How can we train that model?
can we classify between same object of different type , what prompts should we give then? suppose we are trying to classify "severity of burns on skin" and we want to annotate the data with 'severe', 'moderate' or 'slight' burn. can it annotate that ?
Wow! Amazing! You are awesome. Easy to learn project any time😊
Thanks a lot :) I try to do my best. Hearing that it works makes me happy.
fantastic,this moduel is so powerful and useful,3Q
great introduction to Grounding Dino. Some questions I have: how can we train our model to recognize different states of an item e.g. : a car that is missing a wheel, or has its windshield broken ?
DINO model as far as I know it is now just a recognizer not a generator
Thank you for that, very impressive.
Thanks a lot 🙏🏻
Is it possible to extract detections and boundling boxes as separate images ?
Hi 👋🏻! It’s Peter from the video. Do you mean to crop original image using bounding boxes generated with Grounding DINO?
@@Roboflow Once you have detected objects and you have a bounding box, can you extract the bounding box as separate image ?
@@HassanGeedi-h8o you would need to write a small method to crop. We actually have open issue about that: github.com/roboflow/supervision/issues/75 we will add that soon to supervision, but you can take a look at code proposals here.
thanks Piotr, I'm learning a lot from you
My pleasure!
can ı work wıth my own dataset ın that notebook?
This is amazing work, well done! I was wondering what it's performance would look like for real-time crowd detection. Do you have any metrics for running inference on an embedded device such as Jetson?
Unfortunately not, but given the fact that it is only 8fps on A100 GPU I wouldn’t count on any real time applications. I work on few demos. But there are other really cool use cases that I’m exploring.
@@Roboflow Thank you for replying, I had a blast today playing with it and learning about it! Well done :)
@@mariosconstantinou8271 I love to hear that. More cool toys based on Grounding DINO coming soon
Great video!
Very cool AI! Looking forward to see this kind of AI detecting non-coco dataset objects and more specific things, with a prompt.
Looks like we are moving towards that direction quite fast 💨
@@Roboflowtry the gdino 1.5, which get pro and edge two version! I can send you some api if you need:-)
Love it.
Great video, How can I localize or create bounding box on any object around us, I don't want to classify it, it will be used to avoid obstacle
I’m afraid you’ll need to be more specific in your prompt. Did you experimented with our notebook? What was the result?
Much weldone Piotr... As a special request, a video on how to train on custom dataset will be much appreciated
ita a best
Impressive !
I just linked a dataset of images of wheat spikes and it's detecting them! I have a lot to learn because our work so far has been focused on the YOLO family, but this is completely fascinating and out of my scope. Thank you for sharing and please share your insights!
@@lorenzoleongutierrez7927 Hi I guess YOLO architecture is still helpful and needed. Grounding DINO is powerful but slow - only 8 fps on NVIDIA A100. But a strong zero-shot object detector like this can be used for example, to auto-annotate images. As for the theory behind the model I work on blog post right now. It should be out today/tomorrow. Could you share which dataset you used? Is it on Roboflow Universe?
Is there a version with DINO V2? Is DINO V2 for public usage?
We don't have a video on DINOv2 yet, but we have this post as a starting point! blog.roboflow.com/what-is-dinov2/
@@Roboflow 👍 I am already there. Thanks.
awesome, how to save the annotated images in coco format?
Right now you would need to write a ton of Python code to do that. But we actually plan to write simple utils to do that. Stay tuned ;)
@@Roboflow You can do this pretty quickly with voxel fiftyone. I may ping a PR onto the GroundingDino repo with this shortly.
Will submit PR to the repo for this by the end of the day.
@@Georgehwp what are the main differences between cvat and voxel? never heard of voxel before
Very common to use voxel51 and cvat together
When I load Grounding DINO Model this error occurs:
FileNotFoundError: [Errno 2] No such file or directory: '/content/weights/groundingdino_swint_ogc.pth'
Would you help me?
Make sure to run the notebook from top to finish. Looks like you skipped some cell.
@@Roboflow I got it, thanks.
@@ritamonteiro2591 no worries 😉
@@Roboflow One question, can I use this script to classify other types of objects? For example, would I be able to separate oranges from apples?
@@ritamonteiro2591 this use case is actually really simple. I’m sure it can distinguish between oranges and apples.