@@ -58,26 +58,36 @@ Object detection and instance segmentation are by far the most important applica
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5959- [ Visualizing and Evaluating SAHI predictions with FiftyOne] ( https://voxel51.com/blog/how-to-detect-small-objects/ ) (2024) (NEW)
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61- - [ 'Exploring SAHI' Research Article from 'learnopencv.com'] ( https://learnopencv.com/slicing-aided-hyper-inference/ ) (2023) (NEW)
61+ - [ 'Exploring SAHI' Research Article from 'learnopencv.com'] ( https://learnopencv.com/slicing-aided-hyper-inference/ )
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63- - [ 'VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'] ( https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s ) (2023) (NEW )
63+ - [ 'VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'] ( https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s ) (RECOMMENDED )
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6565- [ Video inference support is live] ( https://github.com/obss/sahi/discussions/626 )
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6767- [ Kaggle notebook] ( https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx )
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6969- [ Satellite object detection] ( https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98 )
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71- - [ Error analysis plots & evaluation] ( https://github.com/obss/sahi/discussions/622 ) (NEW )
71+ - [ Error analysis plots & evaluation] ( https://github.com/obss/sahi/discussions/622 ) (RECOMMENDED )
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73- - [ Interactive result visualization and inspection] ( https://github.com/obss/sahi/discussions/624 ) (NEW )
73+ - [ Interactive result visualization and inspection] ( https://github.com/obss/sahi/discussions/624 ) (RECOMMENDED )
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7575- [ COCO dataset conversion] ( https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7 )
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7777- [ Slicing operation notebook] ( demo/slicing.ipynb )
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7979- ` YOLOX ` + ` SAHI ` demo: <a href =" https://huggingface.co/spaces/fcakyon/sahi-yolox " ><img src =" https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg " alt =" sahi-yolox " ></a > (RECOMMENDED)
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81+ - ` RT-DETR ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_rtdetr.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-rtdetr " ></a > (NEW)
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83+ - ` YOLOv8 ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov8.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-yolov8 " ></a > (NEW)
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85+ - ` SuperGradients/YOLONAS ` + ` SAHI ` : <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolonas.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-yolonas " ></a > (NEW)
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87+ - ` DeepSparse ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_sparse_yolov5.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-deepsparse " ></a >
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89+ - ` HuggingFace ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-huggingface " ></a >
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8191- ` YOLOv5 ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-yolov5 " ></a >
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8393- ` MMDetection ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-mmdetection " ></a >
@@ -86,11 +96,6 @@ Object detection and instance segmentation are by far the most important applica
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8797- ` TorchVision ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-torchvision " ></a >
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89- - ` HuggingFace ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-huggingface " ></a > (NEW)
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91- - ` DeepSparse ` + ` SAHI ` walkthrough: <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_sparse_yolov5.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-deepsparse " ></a > (NEW)
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93- - ` SuperGradients/YOLONAS ` + ` SAHI ` : <a href =" https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolonas.ipynb " ><img src =" https://colab.research.google.com/assets/colab-badge.svg " alt =" sahi-yolonas " ></a > (NEW)
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95100<a href =" https://huggingface.co/spaces/fcakyon/sahi-yolox " ><img width =" 600 " src =" https://user-images.githubusercontent.com/34196005/144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif " alt =" sahi-yolox " ></a >
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