Deploying a Deep Learning Model using Hugging Face Spaces and Gradio
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In this computer vision tutorial video, we will see how to deploy a computer vision application. We will deploy an app using Hugging Face Spaces and Gradio. By the end of this video, you'll be able to deploy your model and run inference on both images and videos. Hugging Face Spaces is a collaborative page on the Hugging Face website to deploy and showcase machine learning projects.
❓Deep learning encompasses more than just training a model. Its true value shines through when its potential is leveraged by the masses, be it for image classification or object detection. This is where the importance of deployment is magnified. But worry not, deployment does not necessitate expensive cloud servers. Instead, we can harness the power of Hugging Face Spaces for this purpose, a concept that we will explore extensively in this article by deploying a deep learning model using Hugging Face Spaces and Gradio.
We will delve into the lifecycle of deploying a deep learning model. Our model of choice will be the YOLOv8 Nano model, trained specifically for pothole detection. Additionally, we'll guide you through deploying a model on Hugging Face Spaces capable of running inferences on both images and videos. Brace yourself for an enlightening and intriguing journey into the world of deep learning deployment.
Topics Covered:
✅Body Pose Estimation Using MediaPipe
✅What are Hugging Face Spaces and Gradio and Why Do We Need Them?
✅Deploying DL Models on HuggingFace Spaces and Gradio
✅Deploying the Pothole Detector Deep Learning Model
✅Uploading the Scripts and Model
✅Using the Deployed Pothole Detector App on Hugging Face Spaces
⭐️ Time Stamps:⭐️
00:00-00:21: Introduction
00:21-00:45: Huggingface
00:45-01:15: Gradio
01:15-01:55: Creating a Space
01:55-05:10: Deployment
05:10-05:35: Uploading the Model
05:35-05:52: Outro
Resources:
🖥️ On our blog - learnopencv.com we also share tutorials and code on topics like Image
Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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Пікірлер: 18
love u, u saved me
please can your creat with docker as well
Satya, you have an amazing ability to break down a complex topic into perfectly sized bites that are easy and enjoyable to digest! I've been building Torch/Tensorflow/OpenCV/etc models (including ones on HuggingFace) for a few months now, and having watched & read a myriad of other learning videos/guides I only started to properly understand some of the key concepts once I started following your guides from your website and watching your videos.. I feel very lucky I came across your work a few days ago! Thank you :D Also a question if I may: Do you have any guides (or planning to publish some) about building and deploying similar applications that allow for bulk file upload and inference rather than 1 file at a time? Specifically in the Computer Vision - Document area?
@LearnOpenCV
8 ай бұрын
Thank you for your warm words. I'm glad you find our content useful. Currently we are not planning to build that. But if you want to upload multiple files using Gradio UI, checkout this link: www.gradio.app/docs/file#initialization (Hint: you need file_count = 'multiple'). Next, you can follow this blog post to create your own UI (or rewatch the video :p): learnopencv.com/ai-fitness-trainer-using-mediapipe/ Now if you segment documents from images, you can checkout these two posts: learnopencv.com/deep-learning-based-document-segmentation-using-semantic-segmentation-deeplabv3-on-custom-dataset/ learnopencv.com/automatic-document-scanner-using-opencv/ If you want to process the text in the image, I highly recommend TrOCR: learnopencv.com/trocr-getting-started-with-transformer-based-ocr/
@dedmaroz8943
8 ай бұрын
@@LearnOpenCV thank you so much for your detailed response Satya! I will go through the attached material. Thanks again!
amazing demo, could be nice a video about the docker and the streamlit version. thx again.
@LearnOpenCV
Жыл бұрын
Hey, thank you! You can check the below links for now. Docker: huggingface.co/docs/hub/spaces-sdks-docker Streamlit: huggingface.co/docs/hub/spaces-sdks-streamlit
amazing tutorial can you guide us or provide tutorial-related streamlit?
Very good useful video... kindly create video for docker and streamlit based deployment as well.... thankyou
@LearnOpenCV
11 ай бұрын
Hey! Thank you for your comment. Till we create a video, you can checkout the official huggingface docs. Docker: huggingface.co/docs/hub/spaces-sdks-docker Streamlit: huggingface.co/docs/hub/spaces-sdks-streamlit
If i click a create space it. Shows you have been rate-limted you can retry action later like that ....what can i do
@LearnOpenCV
14 күн бұрын
Hi, did you try their suggestion? discuss.huggingface.co/t/rate-limit-when-using-gradio-and-inference-api/23508/2
Very interesting, but would very much like a written text.
@LearnOpenCV
9 ай бұрын
Hi, please checkout out blogpost for a written explanation: learnopencv.com/deploy-deep-learning-model-huggingface-spaces/
Hello sir, Can we create an automatic annotation in OpenCv , if possible please make a video
@LearnOpenCV
8 ай бұрын
Hi, check out these blogposts: learnopencv.com/automated-image-annotation-tool-using-opencv-python/ learnopencv.com/building-automated-image-annotation-tool-pyopenannotate/
please make a video for deployment on streamlit cloud also
@LearnOpenCV
8 ай бұрын
Thank you for the suggestion. We will surely start working on this topic