Merge branch 'main' into openvino_support_on_yolov8_object_detection

README.md

@@ -44,18 +44,20 @@ Object detection and instance segmentation are by far the most important applica
 
 ## <div align="center">Quick Start Examples</div>
 
-[📜 List of publications that cite SAHI (currently 100+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)
+[📜 List of publications that cite SAHI (currently 150+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)
 
 [🏆 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688)
 
 ### Tutorials
 
 - [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)
 
-- [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral) (NEW)
+- [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral)
 
 - [Pretrained weights and ICIP 2022 paper files](https://github.com/fcakyon/small-object-detection-benchmark)
 
+- [Visualizing and Evaluating SAHI predictions with FiftyOne](https://voxel51.com/blog/how-to-detect-small-objects/) (2024) (NEW)
+
 - ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/) (2023) (NEW)
 
 - ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s) (2023) (NEW)
@@ -82,9 +84,13 @@ Object detection and instance segmentation are by far the most important applica
 
 - `Detectron2` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_detectron2.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-detectron2"></a>
 
+- `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>
+
 - `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)
 
-- `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> (NEW)
+- `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)
+
+- `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) 
 
 <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> 
 
@@ -129,6 +135,12 @@ conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c
 pip install yolov5==7.0.13
 ```
 
+- Install your desired detection framework (ultralytics):
+
+```console
+pip install ultralytics==8.0.207
+```
+
 - Install your desired detection framework (mmdet):
 
 ```console
@@ -148,6 +160,12 @@ pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113
 pip install transformers timm
 ```
 
+- Install your desired detection framework (super-gradients):
+
+```console
+pip install super-gradients==3.3.1
+```
+
 </details>
 
 ### Framework Agnostic Sliced/Standard Prediction
@@ -261,6 +279,20 @@ python -m scripts.run_code_style format
 
 <a align="left" href="https://github.com/tureckova" target="_blank">Alzbeta Tureckova</a>
 
+<a align="left" href="https://github.com/s-aiueo32" target="_blank">So Uchida</a>
+
+<a align="left" href="https://github.com/developer0hye" target="_blank">Yonghye Kwon</a>
+
+<a align="left" href="https://github.com/aphilas" target="_blank">Neville</a>
+
+<a align="left" href="https://github.com/mayrajeo" target="_blank">Janne Mäyrä</a>
+
+<a align="left" href="https://github.com/christofferedlund" target="_blank">Christoffer Edlund</a>
+
+<a align="left" href="https://github.com/ilkermanap" target="_blank">Ilker Manap</a>
+
+<a align="left" href="https://github.com/nguyenthean" target="_blank">Nguyễn Thế An</a>
+
 <a align="left" href="https://github.com/weiji14" target="_blank">Wei Ji</a>
 
 <a align="left" href="https://github.com/aynursusuz" target="_blank">Aynur Susuz</a>
@@ -269,5 +301,13 @@ python -m scripts.run_code_style format
 
 <a align="left" href="https://github.com/lakshaymehra" target="_blank">Lakshay Mehra</a>
 
+<a align="left" href="https://github.com/karl-joan" target="_blank">Karl-Joan Alesma</a>
+
+<a align="left" href="https://github.com/jacobmarks" target="_blank">Jacob Marks</a>
+
+<a align="left" href="https://github.com/williamlung" target="_blank">William Lung</a>
+
+<a align="left" href="https://github.com/amoghdhaliwal" target="_blank">Amogh Dhaliwal</a>
+
 </div>
 

sahi/postprocess/combine.py

@@ -217,19 +217,12 @@ def greedy_nmm(
     # according to their confidence scores
     order = scores.argsort()
 
-    # initialise an empty list for
-    # filtered prediction boxes
-    keep = []
-
     while len(order) > 0:
         # extract the index of the
         # prediction with highest score
         # we call this prediction S
         idx = order[-1]
 
-        # push S in filtered predictions list
-        keep.append(idx.tolist())
-
         # remove S from P
         order = order[:-1]
 

sahi/predict.py

@@ -164,7 +164,7 @@ def get_sliced_prediction(
             detection accuracy. Default: True.
         postprocess_type: str
             Type of the postprocess to be used after sliced inference while merging/eliminating predictions.
-            Options are 'NMM', 'GRREDYNMM' or 'NMS'. Default is 'GRREDYNMM'.
+            Options are 'NMM', 'GREEDYNMM' or 'NMS'. Default is 'GREEDYNMM'.
         postprocess_match_metric: str
             Metric to be used during object prediction matching after sliced prediction.
             'IOU' for intersection over union, 'IOS' for intersection over smaller area.
@@ -231,7 +231,7 @@ def get_sliced_prediction(
     # create prediction input
     num_group = int(num_slices / num_batch)
     if verbose == 1 or verbose == 2:
-        tqdm.write(f"Performing prediction on {num_slices} number of slices.")
+        tqdm.write(f"Performing prediction on {num_slices} slices.")
     object_prediction_list = []
     # perform sliced prediction
     for group_ind in range(num_group):
@@ -416,7 +416,7 @@ def predict(
             Default to ``0.2``.
         postprocess_type: str
             Type of the postprocess to be used after sliced inference while merging/eliminating predictions.
-            Options are 'NMM', 'GREEDYNMM', 'LSNMS' or 'NMS'. Default is 'GRREDYNMM'.
+            Options are 'NMM', 'GREEDYNMM', 'LSNMS' or 'NMS'. Default is 'GREEDYNMM'.
         postprocess_match_metric: str
             Metric to be used during object prediction matching after sliced prediction.
             'IOU' for intersection over union, 'IOS' for intersection over smaller area.
@@ -781,7 +781,7 @@ def predict_fiftyone(
             Default to ``0.2``.
         postprocess_type: str
             Type of the postprocess to be used after sliced inference while merging/eliminating predictions.
-            Options are 'NMM', 'GRREDYNMM' or 'NMS'. Default is 'GRREDYNMM'.
+            Options are 'NMM', 'GREEDYNMM' or 'NMS'. Default is 'GREEDYNMM'.
         postprocess_match_metric: str
             Metric to be used during object prediction matching after sliced prediction.
             'IOU' for intersection over union, 'IOS' for intersection over smaller area.

sahi/utils/coco.py

@@ -130,7 +130,7 @@ def from_coco_annotation_dict(cls, annotation_dict: Dict, category_name: Optiona
             annotation_dict: dict
                 COCO formatted annotation dict (with fields "bbox", "segmentation", "category_id")
         """
-        if annotation_dict.__contains__("segmentation") and not isinstance(annotation_dict["segmentation"], list):
+        if annotation_dict.__contains__("segmentation") and isinstance(annotation_dict["segmentation"], dict):
             has_rle_segmentation = True
             logger.warning(
                 f"Segmentation annotation for id {annotation_dict['id']} is skipped since RLE segmentation format is not supported."