Roboflow
OpenCV Course: Roboflow Overview
updated
- ⭐ supervision: github.com/roboflow/supervision
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Blog post: blog.roboflow.com/best-ocr-models-text-recognition
Workflows: roboflow.com/workflows/build
- 📚 Written guide: blog.roboflow.com/gpt-4o-object-detection
- 📓 GPT-4o fine-tuning API notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/openai-gpt-4o-fine-tuning.ipynb
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
- 📓 object detection fine-tuning notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb
- 📓 instance segmentation fine-tuning notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-instance-segmentation-on-custom-dataset.ipynb
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Roboflow: roboflow.com
Workflows: roboflow.com/workflows/build
Draw Polygonzones: polygonzone.roboflow.com
Counting Workflow (single line): app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiTjBNbzh0RGJ2d3dOV0ZUNTVjeEkiLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiSW1GTElaU2tHYk55OXpiNFV1cWxNelBScHBRMiIsImlhdCI6MTcyNzQ1NDc2OH0.KRySRMBrL-tom8AoReDEeW44Nh-CPOypwWG7QblP9xo
Counting Workflow (double line): app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoicHJMY2wzRERPakVhczRxdHhvV0UiLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiSW1GTElaU2tHYk55OXpiNFV1cWxNelBScHBRMiIsImlhdCI6MTcyNzQ1NDc5OX0.NNO0p56BwJfkhwXqQeGP84HyiiCqd5NAbcAZJ6ORliA
Time in Zone Workflow: app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoicHc4bDlPb3Z0RnZDSUE3OFhGSVMiLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiSW1GTElaU2tHYk55OXpiNFV1cWxNelBScHBRMiIsImlhdCI6MTcyNzQ1NDgyMH0.UgQ4Y7ZeDsmbv1GBPAqfwwR0Su5rL1ggHRQ7NxfRjU4
Resources
Precision and recall: blog.roboflow.com/precision-and-recall
Mean average precision: blog.roboflow.com/mean-average-precision
Confusion matrix: blog.roboflow.com/what-is-a-confusion-matrix
Model evaluation in Roboflow: blog.roboflow.com/evaluate-roboflow-models
Blogpost: blog.roboflow.com/active-learning-workflow
Resources
Roboflow Workflows: roboflow.com/workflows/build
Workflows Templates: roboflow.com/workflows/templates
Small Objects Workflow: app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiMWU4bkNOb2Jtdm0wOGtIb2Q1OWQiLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiSW1GTElaU2tHYk55OXpiNFV1cWxNelBScHBRMiIsImlhdCI6MTcyNDgwNzE0MX0.gqXArEVUQ0tBE7Ja5LMv3pwLY_gtlaPDrA9C_d1RmuA
Segment Anything 2 Workflow: app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiV0tpN0F5YnV3NEtnRjFLN1ZDOTYiLCJ3b3Jrc3BhY2VJZCI6ImtyT1RBYm5jRmhvUU1DZExPbGU0IiwidXNlcklkIjoiSW1GTElaU2tHYk55OXpiNFV1cWxNelBScHBRMiIsImlhdCI6MTcyNDgwNjk2M30.e146HOZSWEbXoZGXxGzI2_EsNcs05kOh-09xIT6w3u4
- 🎬 Football AI video: youtu.be/aBVGKoNZQUw
- ⚽ Roboflow Sports repository: github.com/roboflow/sports
- 🏞️ ball, players, and referees detection dataset: universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc
- 🏞️ pitch keypoints detection dataset: universe.roboflow.com/roboflow-jvuqo/football-field-detection-f07vi
- 📓 ball, players, and referees detection model training notebook: colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_player_detector.ipynb
- 📓 pitch keypoints detection model training notebook: colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_pitch_keypoint_detector.ipynb
- 📓 football AI notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/football-ai.ipynb
- ⚽ Ball Tracking in Sports with Computer Vision blog post: blog.roboflow.com/tracking-ball-sports-computer-vision
- ⚽ Camera Calibration in Sports with Keypoints blog post: blog.roboflow.com/camera-calibration-sports-computer-vision
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00:00 Football (Soccer) AI: The Next Level
- 00:00:58 Architectural Blueprint: Models & Tools for Football AI
- 00:03:14 YOLOv8 Fine-Tuning: Optimizing for Football Object Detection
- 00:12:22 Deploying YOLOv8 with Inference
- 00:27:37 ByteTrack: Robust Multi-Object Tracking
- 00:29:38 Embedding Analysis: Clustering Players with SigLIP & UMAP
- 00:51:46 Perspective Transformation: Homography Fundamentals
- 00:53:03 YOLOv8x-pose Training: Precise Pitch Landmark Detection
- 01:01:24 Keypoint Inference: Real-Time Pitch Understanding
- 01:06:27 Homography Application: Virtual Lines & Field Overlay
- 01:13:23 Top-Down Projection: Creating a Tactical Radar View
- 01:22:04 Spatial Analysis: Ball Territory
- 01:26:34 Implementation Challenges
- 01:28:29 Beyond the Basics: What Else is Possible?
Resources:
- Roboflow: roboflow.com
🔴 Community Session Aug 29th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: youtube.com/watch?v=Xwou5qO--vY
- ⚽ Roboflow Sports repository: github.com/roboflow/sports
- 🏞️ ball, players, and referees detection dataset: universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc
- 🏞️ pitch keypoints detection dataset: universe.roboflow.com/roboflow-jvuqo/football-field-detection-f07vi
- 📓 ball, players, and referees detection model training notebook: colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_player_detector.ipynb
- 📓 pitch keypoints detection model training notebook: colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_pitch_keypoint_detector.ipynb
- 📓 football AI notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/football-ai.ipynb
- ⚽ Ball Tracking in Sports with Computer Vision blog post: blog.roboflow.com/tracking-ball-sports-computer-vision
- ⚽ Camera Calibration in Sports with Keypoints blog post: blog.roboflow.com/camera-calibration-sports-computer-vision
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Sign up for a future session: https://lu.ma/roboflow
- 📓 SAM2 notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-images-with-sam-2.ipynb
- 🗞 SAM2 overview blog post: blog.roboflow.com/what-is-segment-anything-2
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Resources:
- Roboflow: roboflow.com
- 📓 Florence notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb
- 🗞 Florence-2 arXiv paper: arxiv.org/abs/2311.06242
- 🗞 Florence-2 overview blog post: blog.roboflow.com/florence-2
- 🗞 Florence-2 fine-tuning blog post: blog.roboflow.com/fine-tune-florence-2-object-detection
- 🔗 Florence-2 HF Space: huggingface.co/spaces/gokaygokay/Florence-2
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 Introduction: Unlock the Power of Florence-2
- 01:09 Getting Started: Prepare for VLM Fine-Tuning
- 03:55 Florence-2 in Action: Explore Pre-trained Capabilities
- 07:00 Dataset Deep Dive: PyTorch Data Loading for Florence-2
- 13:02 LoRA: Optimize Your VLM Training
- 14:21 Fine-Tuning: Unleash Florence-2's Custom Object Detection
- 17:30 Model Evaluation: Measure Your VLM's Success
- 21:37 Florence-2 vs Other Computer Vision Models
- 24:09 Conclusion and Next Steps
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session July 3th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: https://roboflow.stream
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- 📓 Florence notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb
- 🗞 Florence-2 arXiv paper: arxiv.org/abs/2311.06242
- 🗞 Florence-2 overview blog post: blog.roboflow.com/florence-2
- 🗞 Florence-2 fine-tuning blog post: blog.roboflow.com/fine-tune-florence-2-object-detection
- 🔗 Florence-2 HF Space: huggingface.co/spaces/gokaygokay/Florence-2
- 🗞 Mean Average Precision (mAP) blog post: blog.roboflow.com/mean-average-precision
- 🗞 Confusion Matrix blog post: blog.roboflow.com/what-is-a-confusion-matrix
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Resources:
- Roboflow: roboflow.com
- 🎬 PaliGemma by Google: Train Model on Custom Detection Dataset: youtu.be/OMBmVInx68M
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- 📓 PaliGemma notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb
- 🗞 PaliGemma blog post: blog.roboflow.com/how-to-fine-tune-paligemma
- 🗞 YOLOv10 blog post: blog.roboflow.com/yolov10-how-to-train
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 PaliGemma Capabilities
- 02:03 Environment Setup
- 05:25 Dataset Format
- 09:07 Downloading Pre-trained Model
- 11:27 Loading Dataset
- 13:45 Training and Evaluating the Model
- 15:19 Deploying the Model
- 17:37 Important Considerations
- 20:02 Outro
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session June 6th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: https://roboflow.stream
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- 📓 PaliGemma notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-paligemma-on-detection-dataset.ipynb
- 🗞 Gemma arXiv paper: arxiv.org/pdf/2403.08295
- 🗞 SigLIP arXiv paper: arxiv.org/pdf/2303.15343
- 🗞 PaliGemma blog post: blog.roboflow.com/how-to-fine-tune-paligemma
- 🔗 RF100: rf100.org
- 🔗 PaliGemma model card: kaggle.com/models/google/paligemma
- 🔗 PaliGemma fine-tuned checkpoints: huggingface.co/collections/google/paligemma-ft-models-6643b03efb769dad650d2dda
- 🔗 PaliGemma HF Space: huggingface.co/spaces/big-vision/paligemma
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Resources:
- Roboflow: roboflow.com
- 🎬 Dwell Time Analysis with Computer Vision | Real-Time Stream Processing: youtu.be/hAWpsIuem10
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- 💻 Time in Zone Code: github.com/roboflow/supervision/tree/develop/examples/time_in_zone
- 🌇 MS COCO Dataset on Roboflow Universe: universe.roboflow.com/microsoft/coco/dataset/5
- 📙Detect and Annotate Supervision Guide: supervision.roboflow.com/develop/how_to/detect_and_annotate/#annotate-image-with-segmentations
- 🎬 Source Checkouts Video: youtube.com/watch?v=-8zyEwAa50Q
- 🎬 Source Traffic Video: youtube.com/watch?v=MNn9qKG2UFI
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 Intro
- 00:41 Static File Processing vs. Stream Processing: Time Calculation Explained
- 04:29 Time Calculation Methods: FPS vs. ClockTime
- 06:54 Project Setup
- 08:39 Object Detection and Tracking
- 12:57 Defining Zones: How to Filter Objects
- 16:33 Measuring Time
- 17:44 Why Naive Stream Processing Fails
- 21:02 Efficient Stream Processing
- 24:19 Important Considerations
- 26:07 Outro
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session April 11 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: youtube.com/watch?v=u7XUC-3TqY8
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- 💻 Time in Zone Code: github.com/roboflow/supervision/tree/develop/examples/time_in_zone
- 🌇 MS COCO Dataset on Roboflow Universe: universe.roboflow.com/microsoft/coco/dataset/5
- 📙Detect and Annotate Supervision Guide: supervision.roboflow.com/develop/how_to/detect_and_annotate/#annotate-image-with-segmentations
- 🎬 Source Checkouts Video: youtube.com/watch?v=-8zyEwAa50Q
- 🎬 Source Traffic Video: youtube.com/watch?v=MNn9qKG2UFI
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Resources:
- Roboflow: roboflow.com
- 🎬 YOLOv9 Tutorial: Train Model on Custom Dataset: youtu.be/XHT2c8jT3Bc
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- ⭐ YOLOv9 GitHub: github.com/WongKinYiu/yolov9
- 🗞 YOLOv9 arXiv paper: arxiv.org/abs/2402.13616
- 🗞 YOLOv9 blog post: blog.roboflow.com/train-yolov9-model
- 📓 YOLOv9 notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb?ref=blog.roboflow.com
- ⚽ Football Players Detection dataset: universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc?ref=blog.roboflow.com
- 🤗 YOLO ARENA Space: huggingface.co/spaces/SkalskiP/YOLO-ARENA
- 🤗 YOLO-World + EfficientSAM HF Space: huggingface.co/spaces/SkalskiP/YOLO-World
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Get hands-on with YOLOv9! This video dives into the architecture, setup, and how to train YOLOv9 on your custom datasets.
Chapters:
- 00:00 Intro
- 00:36 Setting Up YOLOv9
- 03:29 YOLOv9 Inference with Pre-Trained COCO Weights
- 06:35 Training YOLOv9 on Custom Dataset
- 10:44 YOLOv9 Model Evaluation
- 13:53 YOLOv9 Inference with Fine-Tuned Model
- 15:18 Model Deployment with Inference Package
- 17:37 Important Considerations for Using YOLOv9
- 19:00 Demo: Build Self-Service Checkout with YOLOv9
- 19:50 Outro
Resources:
Object Detection Model Leaderboard: roboflow.com/models/leaderboard
- Roboflow: roboflow.com
- Roboflow Universe: universe.roboflow.com
- 🔴 Community Session March 14 2024 at 09:00 AM PST / 12:00 PM EST / 06:00 PM CET: https://roboflow.stream
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- ⭐ YOLOv9 GitHub: github.com/WongKinYiu/yolov9
- 🗞 YOLOv9 arXiv paper: arxiv.org/abs/2402.13616
- 🗞 YOLOv9 blog post: blog.roboflow.com/train-yolov9-model
- 📓 YOLOv9 notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb
- ⚽ Football Players Detection dataset: universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc
- 🤗 YOLO ARENA Space: huggingface.co/spaces/SkalskiP/YOLO-ARENA
- 🤗 YOLO-World + EfficientSAM HF Space: huggingface.co/spaces/SkalskiP/YOLO-World
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Resources:
- Roboflow: roboflow.com
- ⭐ YOLO-World GitHub: github.com/AILab-CVC/YOLO-World
- 🗞 YOLO-World arXiv paper: arxiv.org/abs/2401.17270
- 🗞 YOLO-World blog post: blog.roboflow.com/what-is-yolo-world
- 📓 YOLO-World notebook: supervision.roboflow.com/develop/notebooks/zero-shot-object-detection-with-yolo-world
- 🤗 YOLO-World + EfficientSAM HF Space: huggingface.co/spaces/SkalskiP/YOLO-World
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Supervision GitHub: github.com/roboflow/supervision
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 Intro
- 00:42 YOLO-World vs. Traditional Object Detectors: Speed and Accuracy
- 02:26 YOLO-World Architecture - prompt-then-detect
- 03:59 Setting Up and Running YOLO-World
- 05:33 Prompt Engineering and Detections Post-Processing
- 09:20 Video Processing with YOLO-World
- 13:16 Important Considerations for Using YOLO-World
- 15:08 Beyond the Basics: Advanced use cases and future potential
- 16:37 Outro
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session February 27 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: youtube.com/live/lF1BtQL16l4?si=f9sPd4DrX8tnDtc1
- ⭐ Inference GitHub: github.com/roboflow/inference
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- ⭐ YOLO-World GitHub: github.com/AILab-CVC/YOLO-World
- 🗞 YOLO-World arXiv paper: arxiv.org/abs/2401.17270
- 🗞 YOLO-World blog post: blog.roboflow.com/what-is-yolo-world
- 📓 YOLO-World notebook: supervision.roboflow.com/develop/notebooks/zero-shot-object-detection-with-yolo-world
- 🤗 YOLO-World + EfficientSAM HF Space: huggingface.co/spaces/SkalskiP/YOLO-World
- 🗞 GroundingDINO blog post: blog.roboflow.com/grounding-dino-zero-shot-object-detection
- 🗞 Non-Max Suppression blog post: blog.roboflow.com/how-to-code-non-maximum-suppression-nms-in-plain-numpy
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Use this knowledge to enhance traffic control systems, monitor road conditions, and gain valuable insights into vehicle behavior.
Chapters:
- 00:00 Intro
- 00:36 Object Detection
- 03:43 Multi-Object Tracking
- 05:11 Filtering Detections with Polygon Zone
- 06:39 Math Behind Perspective Transformation
- 14:35 Perspective Transformation in Code
- 16:46 Math Behind Speed Estimation
- 18:42 Speed Estimation in Code
- 21:29 Visualization Improvements
- 22:45 Final Results
Resources:
- Roboflow: roboflow.com
- 💻 Speed Estimation Open-Source Code: github.com/roboflow/supervision/tree/develop/examples/speed_estimation
- 📚 "How to Estimate Speed with Computer Vision" blog.roboflow.com/estimate-speed-computer-vision
- 📓Colab Notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-estimate-vehicle-speed-with-computer-vision.ipynb?ref=blog.roboflow.com
- ⭐ Supervision GitHub: github.com/roboflow/supervision
- ⭐ Inference GitHub: github.com/roboflow/inference
- 📚 “How to Track Objects” Supervision Docs: supervision.roboflow.com/how_to/track_objects
- 📚 “Annotators” Supervision Docs: supervision.roboflow.com/annotators
- 🎬 “Track & Count Objects using YOLOv8 ByteTrack & Supervision” YouTube video: youtu.be/OS5qI9YBkfk
- 🎬 “Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking” YouTube video: youtu.be/4Q3ut7vqD5o
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 Intro
- 00:40 Introduction to CogVLM
- 01:43 Setting Up the AWS Infrastructure
- 03:56 Configuring the Inference Server
- 05:41 Running Inference and Testing the Model
- 09:08 Outro
Resources:
- Roboflow: roboflow.com
- Roboflow Universe: universe.roboflow.com
- How to Deploy CogVLM on AWS blog post: blog.roboflow.com/how-to-deploy-cogvlm-in-aws
- GPT-4 Vision Alternatives blog post: blog.roboflow.com/gpt-4-vision-alternatives
- Inference Server code: github.com/roboflow/inference
- CogVLM Client code: github.com/roboflow/cog-vlm-client
- CogVLM: Visual Expert for Pretrained Language Models arXiv paper: arxiv.org/abs/2311.03079
- CogVLM code: github.com/THUDM/CogVLM
- Multimodal Maestro GitHub: github.com/roboflow/multimodal-maestro
- Multimodal Maestro: Advanced LMM Prompting blog post: blog.roboflow.com/multimodal-maestro-advanced-lmm-prompting
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
paint.wtf: https://paint.wtf
Inference: roboflow.com/inference
Discover the top object detection models in 2023 in this comprehensive video. We compare models like YOLOv8, YOLOv7, RTMDet, DETA, DINO, and GroundingDINO based on metrics like Mean Average Precision, community support, packaging, and licensing for you to decide which is best for your production AI applications. The video also details the challenges in comparing model speed and highlights important nuances within the realm of object detection, like choosing the right model for the right hardware and use case. It's an essential watch for anyone interested in computer vision and model selection. This research was sponsored by Intel.
#ObjectDetection #ComputerVision
Chapters:
- 00:00 Introduction
- 00:35 Object Detection
- 01:42 Mean Average Precision
- 02:28 Speed
- 03:40 Paper, Packaging, and License
- 04:35 YOLOv8
- 05:21 YOLOv7
- 06:06 YOLOv6-v3
- 07:01 RTMDet
- 07:46 RT-DETR
- 08:50 DETA
- 10:02 GroundingDINO
- 10:37 Model Community Comparison
- 11:46 Conclusion
Resources:
Object Detection Model Leaderboard: roboflow.com/models/leaderboard
- 🌏 Roboflow: roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 📈GPU vs Intel HPU (new hardware options for AI): blog.roboflow.com/gpu-vs-hpu
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Chapters:
- 00:00 Introduction
- 01:16 Vehicle detection on aerial images using YOLOv8
- 05:03 Tracking objects using ByteTrack and Supervision
- 06:46 Defining entry and exit zones
- 10:28 Assigning vehicles to specific entry zones
- 16:22 Drawing the path of moving objects
- 17:18 Analysing traffic flow
- 22:23 Conclusions
Resources:
- 🌏 Roboflow: roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 💻 Supervision GitHub repository: github.com/roboflow/supervision
- 🖼️ Detecting Vehicles on Aerial Images dataset: universe.roboflow.com/roboflow-jvuqo/detecting-vehicles-on-aerial-images
- 🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset YouTube video: youtu.be/wuZtUMEiKWY?si=iYpuHlGcFTJIzl4c
- 🎬 Track & Count Objects using YOLOv8 ByteTrack & Supervision YouTube video: youtu.be/OS5qI9YBkfk?si=3oNbVxB0m66VTO75
🎬 Count People in Zone | Using YOLOv5, YOLOv8, and Detectron2 | Computer Vision YouTube video: youtu.be/l_kf9CfZ_8M?si=nHJIzu2R2vVQXj2v
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
- 00:00 Introduction
- 00:29 What is MMDetection and RTMDet
- 01:24 OpenMMLab Libraries Installation
- 04:54 Inference with Pre-trained COCO Model
- 08:14 Downloading a Dataset from Roboflow Universe
- 10:02 Preparing Custom MMDetection Configuration File
- 11:30 Train RTMDet and Analyze the Metrics
- 13:08 Evaluating the RTMDet Model with Supervision
- 14:24 Conclusions
Resources:
- 🌏 Roboflow: roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 📓 RTMDet Object Detection Google Colab: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-rtmdet-object-detection-on-custom-data.ipynb
- 🗞️ RTMDet: An Empirical Study of Designing Real-Time Object Detectors Arxiv paper: arxiv.org/pdf/2212.07784.pdf
- 🗞️How to Train RTMDet on a Custom Dataset blog post: blog.roboflow.com/how-to-train-rtmdet-on-a-custom-dataset
- 💻 MMDetection GitHub repository: github.com/open-mmlab/mmdetection
- 🖼️ Double Twelve Dominoes object detection dataset: universe.roboflow.com/pip-tracker/double-twelve-dominoes
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Today, we are open-sourcing the Roboflow Inference Server: our battle-hardened solution for using and deploying computer vision models in production, and announcing Roboflow Inference, an opinionated framework for creating standardized APIs around computer vision models.
Roboflow Deploy powers millions of daily inferences across thousands of models for hundreds of customers (including some of the world’s largest companies), and now we’re making the core technology available to the community under a permissive, Apache 2.0 license.
Chapters:
- 00:00 Introduction
- 01:17 Docker
- 03:24 Image Client
- 08:14 Video Client
- 09:58 Models
- 11:19 Outro
Resources:
- 🌏 Roboflow: roboflow.com
- 🖼️ Roboflow Inference Repository: github.com/roboflow/inference
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 🗞️ Open Source Computer Vision Deployment with Roboflow Inference Blog Post: blog.roboflow.com/open-source-inference-server
- 🚀 Inference Client Examples Repository: github.com/roboflow/inference-client
Remember to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
We compare Segment Anything (SAM) and FastSAM, highlighting their differences in performance and application. We take you through the installation process, inference modes, and deep comparison of these two models, offering insightful observations about their strengths and limitations.
This video serves as a comprehensive guide, packed with valuable resources for FastSAM and SAM, ideal for AI enthusiasts, developers, and researchers alike. Don't miss the opportunity to deepen your understanding of AI, computer vision, and the latest tech breakthroughs!
Chapters:
- 00:00 Introduction
- 01:07 Fast SAM
- 02:10 Installation
- 04:35 Everything Prompt
- 07:36 Box Prompt
- 09:01 Point Prompt
- 09:42 Text Prompt
- 11:05 SAM vs FastSAM
- 13:06 Conclusions
- 15:28 Outro
Resources:
- 🌏 Roboflow: roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 🗞️How to Use FastSAM blogpost:blog.roboflow.com/how-to-use-fastsam
- 🗞️What is FastSAM? The Ultimate Guide. blogpost: blog.roboflow.com/what-is-fastsam
- 🗞️Fast Segment Anything (FastSAM) paper: arxiv.org/abs/2306.12156
- 📓Fast Segment Anything Model (FastSAM) notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-fast-sam.ipynb
- 🌌 SA-1B dataset: segment-anything.com/dataset/index.html
Complementary SAM resources:
On July 29th, 2024, Meta AI released Segment Anything 2 (SAM 2), a new image and video segmentation foundation model. According to Meta, SAM 2 is 6x more accurate than the original SAM model at image segmentation tasks. Learn more: blog.roboflow.com/what-is-segment-anything-2
- 🗞️How to Use the Segment Anything Model (SAM) blog post: blog.roboflow.com/how-to-use-segment-anything-model-sam
- 🎬 Segment Anything Model (SAM) YouTube video: youtu.be/D-D6ZmadzPE
- 🎬 Accelerate Image Annotation with SAM and Grounding DINO YouTube video: youtu.be/oEQYStnF2l8
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Experience CVPR 2023 like never before! As a first-time attendee, I bring you an in-depth review and highlights from one of the most significant events in the Computer Vision community. This video gives you an overview of the conference, intriguing workshops, and a rundown of top papers that have made waves in the field this year.
With over 9000 papers submitted and around 2300 accepted, CVPR 2023 was an event packed with insightful presentations, innovative ideas, and cutting-edge AI technology discussions. Whether you're an AI enthusiast, a practitioner, or a researcher, this video will provide you with the key takeaways from the conference. Don't miss the chance to virtually meet some of the authors behind your favorite models like GroundingDINO, SAM, and OneFormer. Stay tuned till the end to hear some engaging interviews and final thoughts.
Chapters:
00:00 Introduction
00:58 Conference Overview
02:20 Workshops
03:39 Papers Overview
04:44 Top Papers
08:13 Final Thoughts
08:48 Interviews
Resources:
- 🌏 Roboflow: roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 🌌 Roboflow Universe: universe.roboflow.com
- 🗞️ CVPR 2023 Highlights blogpost: blog.roboflow.com/cvpr-2023-highlights
- 🗞️ Roboflow 100 blogpost: blog.roboflow.com/roboflow-100
- 🗞️ List of Accepted CVPR 2023 papers: cvpr2023.thecvf.com/Conferences/2023/AcceptedPapers
- 🗞️ Computer Vision in the Wild CVPR 2023 workshop overview: computer-vision-in-the-wild.github.io/cvpr-2023
- 🎬 Grounding DINO YouTube video: youtu.be/cMa77r3YrDk
- 🎬 Segment Anything Model (SAM) YouTube video: youtu.be/D-D6ZmadzPE
- 🎬 OneFormer YouTube video: youtu.be/_Zr1pOi7Chw
- 🎬 RF100 YouTube video: youtu.be/i8amBaHrhqY
- 🗞️ ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models paper: arxiv.org/abs/2303.04803
- 🗞️ OneFormer: One Transformer to Rule Universal Image Segmentation paper: arxiv.org/abs/2211.06220
- 🗞️ PACO: Parts and Attributes of Common Objects paper: arxiv.org/abs/2301.01795
- 🗞️ DynIBaR Neural Dynamic Image-Based Rendering paper: arxiv.org/abs/2211.11082
- 🗞️ SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields paper: arxiv.org/abs/2211.12254
- 🗞️ InstructPix2Pix: Learning to Follow Image Editing Instructions paper: arxiv.org/abs/2211.12254
- 🗞️ DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation paper: arxiv.org/abs/2208.12242
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
#CVPR2023 #ComputerVision #CVPRHighlights #PatternRecognition #ObjectDetection #InstanceSegmentation #CVPRPapers
- Google Colab: drive.google.com/file/d/1uWPISt3teYe2VAq05gcxoUIXah3_xBJQ/view?usp=sharing
Chapters:
00:00 Autodistill Overview
00:50 Setup and Installation
01:50 Import Dataset
03:00 Initialize Base Model and Autolabel
04:50 Initialize Target Model and Train
07:10 Visualize the Results
Resources:
- Autodistill Repository: github.com/autodistill/autodistill
- Autodistill Overview: blog.roboflow.com/autodistill
- Roboflow: roboflow.com
- Roboflow Universe: universe.roboflow.com
- Roboflow Notebooks Repository: github.com/roboflow/notebooks
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest in ML!
Stay updated with the projects I'm working on at github.com/roboflow and github.com/artyaltanzaya
Autodistill is a ground-breaking tool revolutionizing the world of computer vision! In this video, we will show you how to use a new library to train a YOLOv8 model to detect bottles moving on a conveyor line. Yes, that's right - zero annotation hours are required! We dive deep into Autodistill's functionality, covering topics from setting up your Python environment and preparing your images, to the thrilling automatic annotation of images.
What makes Autodistill a game-changer is its ability to distill knowledge from large foundational models like GroundedSAM, transferring this knowledge into highly-optimized computer vision models. We're ecstatic to showcase how this library can turn hours of tedious manual annotation into a fully automated process, without compromising the accuracy of your models.
Chapters:
00:00 Autodistill Overview
02:03 Project Overview
03:16 Setup Python Environment
04:46 Prepare Images
06:22 Autoannotate Images
07:50 Train YOLOv8 Model
08:28 Run Inference on Video
09:16 Conclusion
Resources:
- 🌏 Roboflow: roboflow.com
- 🧪 Autodistill Repository: github.com/autodistill/autodistill
- 🌌 Roboflow Universe: universe.roboflow.com
- 📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
- 📓 How to Auto Train YOLOv8 Model with Autodistill Google Colab: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-auto-train-yolov8-model-with-autodistill.ipynb
- 🗞️ Autodistill blogpost: blog.roboflow.com/autodistill
- 🎬 How to Train YOLOv8 Object Detection Model on a Custom Dataset YouTube video: youtu.be/wuZtUMEiKWY
- 🎬 How to Train YOLOv8 Instance Segmentation Model on Custom Dataset YouTube video: youtu.be/pFiGSrRtaU4
- 🎬 Detect Anything You Want with Grounding DINO YouTube video: youtu.be/cMa77r3YrDk
- 🎬 SAM - Segment Anything Model Overview YouTube video: youtu.be/D-D6ZmadzPE
- 🎬 Accelerate Image Annotation with SAM and Grounding DINO YouTube video: youtu.be/oEQYStnF2l8
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
From the importance of high-quality datasets to hardware considerations, interoperability, benchmarking, and licensing issues, this video covers it all. Whether you're planning to develop an app for counting commuters in public transport or analyzing medical images, we guide you on the critical factors that should inform your model selection. We even explore specific models like YOLOv5, YOLO-NAS, and Detectron2 in context. Don't forget to like, subscribe, and stay tuned for more computer vision content!
Chapters:
00:00 Introduction
00:40 Overthinking Model Selection
01:36 Different Project Contexts (Counting People vs Analyzing Medical Images)
03:15 Hardware Considerations
04:04 mAP vs Latency
05:33 Benchmarking and the Importance of Preliminary Testing
06:00 Understanding mAP Values in the Context of Custom Datasets
08:27 Library Packaging
09:46 Model Integration and the Role of SDKs
10:52 Importance of Active Project Support
11:27 Understanding Project Licenses
12:31 Conclusion
Resources:
Object Detection Model Leaderboard: roboflow.com/models/leaderboard
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: youtu.be/wuZtUMEiKWY
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
#ComputerVision #ObjectDetection #InstanceSegmentation #DeepLearning #YOLO #Detectron2 #Dataset #ModelSelection #AI #YOLOv8
Chapters:
00:00 Introduction and Model Overview
01:57 Python Environment Setup and Installing YOLO-NAS
04:56 Inference with pre-trained COCO model
07:21 YOLO-NAS Inference Output Format
09:06 Finding Open-source Datasets
10:19 Training YOLO-NAS on Custom Dataset
16:34 Evaluate Trained Model
17:28 Inference with Trained Model
18:39 Conclusion
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
📚 How to Train YOLO-NAS on a Custom Dataset blog post: blog.roboflow.com/yolo-nas-how-to-train-on-custom-dataset
📓 How to Train YOLO-NAS on a Custom Dataset notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo-nas-on-custom-dataset.ipynb
❓ Why do we need to restart Google Colab? github.com/obss/sahi/discussions/781
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: youtu.be/wuZtUMEiKWY
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
#YOLO_NAS #Deci #ObjectDetection #NeuralArchitectureSearch #Python #COCO #MachineLearning #ArtificialIntelligence #CustomDataset #Inference #ModelEvaluation #OpenSource #Datasets
Start your Data Science and Computer Vision adventure with this comprehensive Image Embedding and Vector Analysis guide. Explore OpenAI CLIP embeddings for image clustering and duplicate detection, and learn essential concepts like T-SNE, UMAP, and MNIST. Follow our beginner-friendly Google Colab notebook to master the basics and kickstart your journey in Computer Vision!
Chapters:
00:00 Introduction
01:23 Python Environment Setup
01:58 Clustering MNIST images using pixel brightness
09:00 T-SNE vs. UMAP
10:40 Clustering images using OpenAI CLIP embeddings
17:22: Using OpenAI CLIP embeddings to detect duplicates or close duplicates
20:05 Conclusions
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📓 "Image Embeddings Analysis notebook": colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/image_embeddings_analysis_part_1.ipynb
📚 "What is OpenAI's CLIP and how to use it?" blog post: blog.roboflow.com/openai-clip
🎬 "CLIP: OpenAI's amazing new zero-shot image classifier" video: youtu.be/8o701AEoZ8I
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
ComputerVision #DataScience #ImageEmbeddings #OpenAICLIP #TSNE #UMAP #MNIST
In this comprehensive tutorial, discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8. Stay tuned for the upcoming release of a Python library that will make this process even more effortless.
Chapters:
00:00 Introduction
00:58 Python Environment Setup
04:13 Load Grounding DINO and Segment Anything Models
05:42 Single Image Mask Autoannotation
08:24 Full Dataset Mask Autoannotation
09:58 Save Labels to Pascal VOC XML
14:17 Upload Annotations to Roboflow
15:23 Review and Refine Annotations in Roboflow UI
17:11 Convert Object Detection to Instance Segmentation Dataset
22:35 Conclusions
23:28 Announcement
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📓 Automated Dataset Annotation and Evaluation with Grounding DINO and SAM notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb
📚 Grounding DINO blog post: blog.roboflow.com/grounding-dino-zero-shot-object-detection
🎬 Detect Anything You Want with Grounding DINO | Zero-Shot Object Detection SOTA video: youtu.be/cMa77r3YrDk
📚 How to Use the Segment Anything Model (SAM) blog post: blog.roboflow.com/how-to-use-segment-anything-model-sam
🎬 SAM - Segment Anything Model by Meta AI: Complete Guide | Python Setup & Applications video: youtu.be/D-D6ZmadzPE
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀
#MetaAI #SegmentAnythingModel #SAM #ImageSegmentation #Python #FoundationModels #ComputerVision #ZeroShot #GroundingDINO #ObjectDetection #DataLabeling
Using the Smart Polygon feature, you’re accessing a cloud-hosted Segment Anything model enabling you to apply polygon annotations faster, easier, and more accurately than ever before, right inside the Roboflow UI. No setup. No servers. No integrations. Create annotations with one click.
Chapters:
00:00 - Introduction
00:35 - Concrete Cracks Annotation with Smart Polygon
03:06 - Converting Bounding Box Blueberry Object Detection Dataset into Polygons
06:43 - Annotating Low-Resolution Blood Cells Images with Smart Polygon
08:47 - Conclusion
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📚 Launch: Label Data with Segment Anything in Roboflow blog: blog.roboflow.com/label-data-segment-anything-model-sam
🔥 Automated Data Labeling with SAM: youtu.be/oEQYStnF2l8
On July 29th, 2024, Meta AI released Segment Anything 2 (SAM 2), a new image and video segmentation foundation model. According to Meta, SAM 2 is 6x more accurate than the original SAM model at image segmentation tasks. Learn more: blog.roboflow.com/what-is-segment-anything-2
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest breakthroughs in technology! 🚀
#MetaAI #SegmentAnythingModel #SAM #ImageSegmentation #ComputerVision
Discover the incredible potential of Meta AI's Segment Anything Model (SAM) in this comprehensive tutorial! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results.
🔍 Explore this in-depth guide as we walk you through setting up your Python environment, loading SAM, generating segmentation masks, and much more. Master the art of converting object detection datasets into segmentation masks and learn how to leverage this powerful tool for your projects.
Chapters:
00:00 - Introduction and Overview of SAM by Meta AI
01:00 - Setting up Your Python Environment
02:46 - Loading the Segment Anything Model
03:09 - Automated Mask Generation with SAM
06:36 - Generate Segmentation Mask with Bounding Box
10:02 - Convert Object Detection Dataset into Segmentation Masks
12:01 - Outro
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
✏️ Roboflow Annotate power by SAM: blog.roboflow.com/label-data-segment-anything-model-sam
📚 How to Use the Segment Anything Model (SAM) blog post: blog.roboflow.com/how-to-use-segment-anything-model-sam
📓 How to Segment Anything with SAM notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb
🎬 Segment Anything Model (SAM) Breakdown YouTube video: youtu.be/umurJ8GsuH0
🔥 Automated Data Labeling with SAM: youtu.be/oEQYStnF2l8
💻 Segment Anything Model repository: github.com/facebookresearch/segment-anything
🌌 Access the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images: segment-anything.com/dataset/index.html
📚 Segment Anything arXiv paper: arxiv.org/pdf/2304.02643.pdf
On July 29th, 2024, Meta AI released Segment Anything 2 (SAM 2), a new image and video segmentation foundation model. According to Meta, SAM 2 is 6x more accurate than the original SAM model at image segmentation tasks. Learn more: blog.roboflow.com/what-is-segment-anything-2
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest breakthroughs in technology! 🚀
#MetaAI #SegmentAnythingModel #SAM #ImageSegmentation #Python #FoundationModels #ComputerVision #ZeroShot
Accompanying blog post: blog.roboflow.com/segment-anything-breakdown
Use SAM in Roboflow to label data: blog.roboflow.com/label-data-segment-anything-model-sam
Automated data labeling with SAM: youtu.be/oEQYStnF2l8
Interactive Segment Anything Experience: segment-anything.com
Github repo: github.com/facebookresearch/segment-anything
arXiv paper: arxiv.org/abs/2304.02643
How to use SAM: blog.roboflow.com/how-to-use-segment-anything-model-sam
On July 29th, 2024, Meta AI released Segment Anything 2 (SAM 2), a new image and video segmentation foundation model. According to Meta, SAM 2 is 6x more accurate than the original SAM model at image segmentation tasks. Learn more: blog.roboflow.com/what-is-segment-anything-2
Chapters:
00:00 Introduction
01:06 Notebook Overview
01:34 Setting up the Python environment
02:35 Load Grounding DINO model
03:48 Download Dataset from Roboflow Universe
04:44 Single Image Model Evaluation
05:35 Evaluate Grounding DINO on Custom Dataset
10:30 Dataset Auto Annotation with Grounding DINO
13:18 Outro
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📓 Automated Dataset Annotation and Evaluation with Grounding DINO notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino.ipynb
🔥 Automated Dataset Annotation with Segment Anything and Grounding DINO: youtu.be/oEQYStnF2l8
🗞 Grounding DINO blog post: blog.roboflow.com/grounding-dino-zero-shot-object-detection
🗞 Grounding DINO arXiv paper: arxiv.org/abs/2303.05499
💻 Grounding DINO repository: github.com/IDEA-Research/GroundingDINO
🎬 Detect Anything You Want with Grounding DINO | Zero-Shot Object Detection SOTA: youtu.be/cMa77r3YrDk
🎬 GPT 4: Will We Ever Train Again?: youtu.be/aNLl0wEdMq4
🎬 CLIP: OpenAI's amazing new zero-shot image classifier: youtu.be/8o701AEoZ8I
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Chapters:
00:00 Introduction
01:29 Setting up the Python environment
03:11 Loading GroundingDINO model
04:38 Multimodal deep learning
05:23 Running GroundingDINO on custom images
06:21 Prompt engineering and object detection language constraints
07:36 Multiclass detection
09:12 More object detection language constraints
11:19 Dataset auto labeling with Roboflow and GroundingDINO
12:48 Outro
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📓 Grounding DINO notebook: github.com/roboflow/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb
🗞 Grounding DINO blog post: blog.roboflow.com/grounding-dino-zero-shot-object-detection
🗞 Grounding DINO arXiv paper: arxiv.org/abs/2303.05499
💻 Grounding DINO repository: github.com/IDEA-Research/GroundingDINO
🎬 GPT 4: Will We Ever Train Again?: youtu.be/aNLl0wEdMq4
🎬 CLIP: OpenAI's amazing new zero-shot image classifier: youtu.be/8o701AEoZ8I
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Writing the same code over and over again is exhausting. That’s why we decided to create Supervision, an open-source toolkit for any computer vision project. Whether you want to process a video, draw a detection on a frame, or convert labels from one format to another; we’ve got you covered!
Chapters:
00:00 Introduction
01:06 Documentation and Examples
02:13 Supervision Roadmap
03:17 Outro
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
⭐ Supervision repository: github.com/roboflow/supervision
📄 Supervision documentation: roboflow.github.io/supervision
🎬 Track & Count Objects using YOLOv8 ByteTrack & Supervision: youtu.be/OS5qI9YBkfk
🎬 Count People in Zone | Using YOLOv5, YOLOv8, and Detectron2 | Computer Vision: youtu.be/l_kf9CfZ_8M
🎬 YOLOv8 Object Counting in Real-time with Webcam, OpenCV and Supervision: youtu.be/QV85eYOb7gk
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Here, we share initial speculations on the answers to these questions and reflect on where the industry is heading. Let’s dive in!
Project available on Universe: universe.roboflow.com/corn-counting/money-counter/health
The video will show you how to use the new YOLOv8 object tracking functionality using a live webcam stream.
Whether you're an experienced developer or a beginner in the field of computer vision, this video covers everything you need to know to get started with object tracking using YOLOv8. With step-by-step instructions, you'll learn how to use state-of-the-art deep-learning models to track objects in videos and live streams.
Chapters:
00:00 Introduction
00:44 Setup Python environment
01:28 Real-time object tracking in the terminal with YOLOv8 CLI
02:17 Bulk video tracking with YOLOv8 SDK
04:56 Setting up inference loop with YOLOv8 SDK
06:11 Bounding box annotation with Supervision
08:18 YOLOv8 tracking ⭐
10:26 Real-time object counting with Supervision
12:57 Conclusion
Resources:
⭐ GitHub repository with demo project: github.com/SkalskiP/yolov8-native-tracking
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
⭐ Supervision repository: github.com/roboflow/supervision
🎬 Track & Count Objects using YOLOv8 ByteTrack & Supervision: youtu.be/OS5qI9YBkfk
🎬 Count People in Zone | 3 Models: YOLOv5, YOLOv8 and Detectron2: youtu.be/l_kf9CfZ_8M
🎬 YOLOv8 Object Counting in Real-time with Webcam, OpenCV, and Supervision: youtu.be/QV85eYOb7gk
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: youtu.be/wuZtUMEiKWY
📓 Learn more about YOLOv8 and other Computer Vision models with Roboflow Notebooks: github.com/roboflow/notebooks
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Next, we'll show you how to download custom datasets from Roboflow Universe and build custom PyTorch COCO Detection datasets. We'll also cover how to visualize COCO dataset entries and build custom PyTorch data loaders.
At the core of this tutorial, we'll demonstrate how to build a custom PyTorch Lightning DETR module for training your own object detection model. We'll then guide you through training DETR on your custom dataset, followed by custom DETR model inference and evaluation.
Chapters:
0:00 Introduction
1:06 Setting up the Python environment
3:19 DETR model inference on example images
8:40 Download custom dataset from Roboflow Universe
10:31 Building custom PyTorch COCO Detection datasets
12:25 Visualising COCO datasets entry
13:15 Building custom PyTorch Data Loaders
15:59 Building custom PyTorch Lightning DETR Module
18:50 Training DETR on custom dataset
20:06 Custom DETR model inference
22:07 Evaluating custom DETR model
22:40 Outro
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
🗒️ How to Train DETR with 🤗 Transformers on a Custom Dataset notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-huggingface-detr-on-custom-dataset.ipynb
🤗 Transformers DETR documentation: huggingface.co/docs/transformers/model_doc/detr
🎬 Exploring The COCO Dataset YouTube video: youtu.be/B4gNml3V2cc
⌛ PyTorch Data Loader documentation: pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
⚡PyTorch Lightning Module documentation: pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐