Slicing Aided Hyper Inference for Small Object Detection - SAHI
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Small object detection focuses on identifying and pinpointing tiny objects within digital images. This task is made difficult due to the limited size and pixel representation of these objects, alongside challenges like reduced visibility and a low signal-to-noise ratio.
There are many modern object detection techniques available, such as Faster RCNN, YOLO, SSD, RetinaNet, EfficientDet, and more. Most of these models are trained using the COCO (Common Objects in Context) dataset. This dataset is vast and offers a wide variety of object categories and annotations, making it a prime option for training object detection algorithms. However, a curious observation is that these models often struggle with small object detection. An approach like "Slicing Aided Hyper Inference for Small Object Detection - SAHI" could be a potential solution to this problem. Isn't that thought-provoking?
⭐️ Time Stamps:⭐️
00:00-00:10: Introduction
00:10-00:58: SAHI
00:58-05:30: Code Snippets
05:30-05:44: Outro
Resources:
🖥️ On our blog - https://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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🔖Hashtags🔖
#SAHI #ObjectDetection #SmallObjectDetection #MachineLearning #ArtificialIntelligence #DeepLearning #AI #COCODataset #YOLO #EfficientDet #RetinaNet #SSD #FasterRCNN #AIDetection #opencvunibersity #deeplearning #computervision #learnopencv #opencv
📚 Check out our FREE Courses at OpenCV University : https://opencv.org/university/free-co...
Small object detection focuses on identifying and pinpointing tiny objects within digital images. This task is made difficult due to the limited size and pixel representation of these objects, alongside challenges like reduced visibility and a low signal-to-noise ratio.
There are many modern object detection techniques available, such as Faster RCNN, YOLO, SSD, RetinaNet, EfficientDet, and more. Most of these models are trained using the COCO (Common Objects in Context) dataset. This dataset is vast and offers a wide variety of object categories and annotations, making it a prime option for training object detection algorithms. However, a curious observation is that these models often struggle with small object detection. An approach like "Slicing Aided Hyper Inference for Small Object Detection - SAHI" could be a potential solution to this problem. Isn't that thought-provoking?
⭐️ Time Stamps:⭐️
00:00-00:10: Introduction
00:10-00:58: SAHI
00:58-05:30: Code Snippets
05:30-05:44: Outro
Resources:
🖥️ On our blog - https://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.
🤖 Learn from the experts on AI: Computer Vision and AI Courses
YOU have an opportunity to join the over 5300+ (and counting) researchers, engineers, and students that have benefited from these courses and take your knowledge of computer vision, AI, and deep learning to the next level.🤖
https://opencv.org/courses
#️⃣ Connect with Us #️⃣
📝 Linkedin: LinkedIn: satyamallick
📱 Twitter: Twitter: LearnOpenCV
🔊 Facebook: https://www.facebook.com/profile.php?...
📸 Instagram: Instagram: learnopencv
🔗 Reddit: Reddit: spmallick
🔖Hashtags🔖
#SAHI #ObjectDetection #SmallObjectDetection #MachineLearning #ArtificialIntelligence #DeepLearning #AI #COCODataset #YOLO #EfficientDet #RetinaNet #SSD #FasterRCNN #AIDetection #opencvunibersity #deeplearning #computervision #learnopencv #opencv
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