diff --git a/demo/large_image.jpg b/demo/large_image.jpg
new file mode 100644
index 00000000..1abbc5d9
Binary files /dev/null and b/demo/large_image.jpg differ
diff --git a/demo/large_image_demo.py b/demo/large_image_demo.py
new file mode 100644
index 00000000..9b4a72ac
--- /dev/null
+++ b/demo/large_image_demo.py
@@ -0,0 +1,208 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Perform MMYOLO inference on large images (as satellite imagery) as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth # noqa: E501, E261.
+
+python demo/large_image_demo.py \
+ demo/large_image.jpg \
+ configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+ checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+```
+"""
+
+import os
+from argparse import ArgumentParser
+
+import mmcv
+from mmdet.apis import inference_detector, init_detector
+from mmengine.logging import print_log
+from mmengine.utils import ProgressBar
+
+try:
+ from sahi.slicing import slice_image
+except ImportError:
+ raise ImportError('Please run "pip install -U sahi" '
+ 'to install sahi first for large image inference.')
+
+from mmyolo.registry import VISUALIZERS
+from mmyolo.utils import register_all_modules, switch_to_deploy
+from mmyolo.utils.large_image import merge_results_by_nms
+from mmyolo.utils.misc import get_file_list
+
+
+def parse_args():
+ parser = ArgumentParser(
+ description='Perform MMYOLO inference on large images.')
+ parser.add_argument(
+ 'img', help='Image path, include image file, dir and URL.')
+ parser.add_argument('config', help='Config file')
+ parser.add_argument('checkpoint', help='Checkpoint file')
+ parser.add_argument(
+ '--out-dir', default='./output', help='Path to output file')
+ parser.add_argument(
+ '--device', default='cuda:0', help='Device used for inference')
+ parser.add_argument(
+ '--show', action='store_true', help='Show the detection results')
+ parser.add_argument(
+ '--deploy',
+ action='store_true',
+ help='Switch model to deployment mode')
+ parser.add_argument(
+ '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+ parser.add_argument(
+ '--patch-size', type=int, default=640, help='The size of patches')
+ parser.add_argument(
+ '--patch-overlap-ratio',
+ type=int,
+ default=0.25,
+ help='Ratio of overlap between two patches')
+ parser.add_argument(
+ '--merge-iou-thr',
+ type=float,
+ default=0.25,
+ help='IoU threshould for merging results')
+ parser.add_argument(
+ '--merge-nms-type',
+ type=str,
+ default='nms',
+ help='NMS type for merging results')
+ parser.add_argument(
+ '--batch-size',
+ type=int,
+ default=1,
+ help='Batch size, must greater than or equal to 1')
+ parser.add_argument(
+ '--debug',
+ action='store_true',
+ help='Export debug images at each stage for 1 input')
+ args = parser.parse_args()
+ return args
+
+
+def main():
+ args = parse_args()
+
+ # register all modules in mmdet into the registries
+ register_all_modules()
+
+ # build the model from a config file and a checkpoint file
+ model = init_detector(args.config, args.checkpoint, device=args.device)
+
+ if args.deploy:
+ switch_to_deploy(model)
+
+ if not os.path.exists(args.out_dir) and not args.show:
+ os.mkdir(args.out_dir)
+
+ # init visualizer
+ visualizer = VISUALIZERS.build(model.cfg.visualizer)
+ visualizer.dataset_meta = model.dataset_meta
+
+ # get file list
+ files, source_type = get_file_list(args.img)
+
+ # if debug, only process the first file
+ if args.debug:
+ files = files[:1]
+
+ # start detector inference
+ print(f'Performing inference on {len(files)} images... \
+This may take a while.')
+ progress_bar = ProgressBar(len(files))
+ for file in files:
+ # read image
+ img = mmcv.imread(file)
+
+ # arrange slices
+ height, width = img.shape[:2]
+ sliced_image_object = slice_image(
+ img,
+ slice_height=args.patch_size,
+ slice_width=args.patch_size,
+ auto_slice_resolution=False,
+ overlap_height_ratio=args.patch_overlap_ratio,
+ overlap_width_ratio=args.patch_overlap_ratio,
+ )
+
+ # perform sliced inference
+ slice_results = []
+ start = 0
+ while True:
+ # prepare batch slices
+ end = min(start + args.batch_size, len(sliced_image_object))
+ images = []
+ for sliced_image in sliced_image_object.images[start:end]:
+ images.append(sliced_image)
+
+ # forward the model
+ slice_results.extend(inference_detector(model, images))
+
+ if end >= len(sliced_image_object):
+ break
+ start += args.batch_size
+
+ if source_type['is_dir']:
+ filename = os.path.relpath(file, args.img).replace('/', '_')
+ else:
+ filename = os.path.basename(file)
+
+ # export debug images
+ if args.debug:
+ # export sliced images
+ for i, image in enumerate(sliced_image_object.images):
+ image = mmcv.imconvert(image, 'bgr', 'rgb')
+ out_file = os.path.join(args.out_dir, 'sliced_images',
+ filename + f'_slice_{i}.jpg')
+
+ mmcv.imwrite(image, out_file)
+
+ # export sliced image results
+ for i, slice_result in enumerate(slice_results):
+ out_file = os.path.join(args.out_dir, 'sliced_image_results',
+ filename + f'_slice_{i}_result.jpg')
+ image = mmcv.imconvert(sliced_image_object.images[i], 'bgr',
+ 'rgb')
+
+ visualizer.add_datasample(
+ os.path.basename(out_file),
+ image,
+ data_sample=slice_result,
+ draw_gt=False,
+ show=args.show,
+ wait_time=0,
+ out_file=out_file,
+ pred_score_thr=args.score_thr,
+ )
+
+ image_result = merge_results_by_nms(
+ slice_results,
+ sliced_image_object.starting_pixels,
+ src_image_shape=(height, width),
+ nms_cfg={
+ 'type': args.merge_nms_type,
+ 'iou_thr': args.merge_iou_thr
+ })
+
+ img = mmcv.imconvert(img, 'bgr', 'rgb')
+ out_file = None if args.show else os.path.join(args.out_dir, filename)
+
+ visualizer.add_datasample(
+ os.path.basename(out_file),
+ img,
+ data_sample=image_result,
+ draw_gt=False,
+ show=args.show,
+ wait_time=0,
+ out_file=out_file,
+ pred_score_thr=args.score_thr,
+ )
+ progress_bar.update()
+
+ if not args.show:
+ print_log(
+ f'\nResults have been saved at {os.path.abspath(args.out_dir)}')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/docs/en/user_guides/useful_tools.md b/docs/en/user_guides/useful_tools.md
index a1ec5dfc..b23e0b12 100644
--- a/docs/en/user_guides/useful_tools.md
+++ b/docs/en/user_guides/useful_tools.md
@@ -350,6 +350,59 @@ python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
--output-dir ${OUTPUT_DIR}
```
+## Perform inference on large images
+
+First install [`sahi`](https://github.com/obss/sahi) with:
+
+```shell
+pip install -U sahi>=0.11.4
+```
+
+Perform MMYOLO inference on large images (as satellite imagery) as:
+
+```shell
+wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth
+
+python demo/large_image_demo.py \
+ demo/large_image.jpg \
+ configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+ checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+```
+
+Arrange slicing parameters as:
+
+```shell
+python demo/large_image_demo.py \
+ demo/large_image.jpg \
+ configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+ checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+ --patch-size 512
+ --patch-overlap-ratio 0.25
+```
+
+Export debug visuals while performing inference on large images as:
+
+```shell
+python demo/large_image_demo.py \
+ demo/large_image.jpg \
+ configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py \
+ checkpoint/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth \
+ --debug
+```
+
+[`sahi`](https://github.com/obss/sahi) citation:
+
+```
+@article{akyon2022sahi,
+ title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
+ author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
+ journal={2022 IEEE International Conference on Image Processing (ICIP)},
+ doi={10.1109/ICIP46576.2022.9897990},
+ pages={966-970},
+ year={2022}
+}
+```
+
## Extracts a subset of COCO
The training dataset of the COCO2017 dataset includes 118K images, and the validation set includes 5K images, which is a relatively large dataset. Loading JSON in debugging or quick verification scenarios will consume more resources and bring slower startup speed.
diff --git a/mmyolo/utils/large_image.py b/mmyolo/utils/large_image.py
new file mode 100644
index 00000000..68c6938e
--- /dev/null
+++ b/mmyolo/utils/large_image.py
@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Sequence, Tuple
+
+from mmcv.ops import batched_nms
+from mmdet.structures import DetDataSample, SampleList
+from mmengine.structures import InstanceData
+
+
+def shift_predictions(det_data_samples: SampleList,
+ offsets: Sequence[Tuple[int, int]],
+ src_image_shape: Tuple[int, int]) -> SampleList:
+ """Shift predictions to the original image.
+
+ Args:
+ det_data_samples (List[:obj:`DetDataSample`]): A list of patch results.
+ offsets (Sequence[Tuple[int, int]]): Positions of the left top points
+ of patches.
+ src_image_shape (Tuple[int, int]): A (height, width) tuple of the large
+ image's width and height.
+ Returns:
+ (List[:obj:`DetDataSample`]): shifted results.
+ """
+ try:
+ from sahi.slicing import shift_bboxes, shift_masks
+ except ImportError:
+ raise ImportError('Please run "pip install -U sahi" '
+ 'to install sahi first for large image inference.')
+
+ assert len(det_data_samples) == len(
+ offsets), 'The `results` should has the ' 'same length with `offsets`.'
+ shifted_predictions = []
+ for det_data_sample, offset in zip(det_data_samples, offsets):
+ pred_inst = det_data_sample.pred_instances.clone()
+
+ # shift bboxes and masks
+ pred_inst.bboxes = shift_bboxes(pred_inst.bboxes, offset)
+ if 'masks' in det_data_sample:
+ pred_inst.masks = shift_masks(pred_inst.masks, offset,
+ src_image_shape)
+
+ shifted_predictions.append(pred_inst.clone())
+
+ shifted_predictions = InstanceData.cat(shifted_predictions)
+
+ return shifted_predictions
+
+
+def merge_results_by_nms(results: SampleList, offsets: Sequence[Tuple[int,
+ int]],
+ src_image_shape: Tuple[int, int],
+ nms_cfg: dict) -> DetDataSample:
+ """Merge patch results by nms.
+
+ Args:
+ results (List[:obj:`DetDataSample`]): A list of patch results.
+ offsets (Sequence[Tuple[int, int]]): Positions of the left top points
+ of patches.
+ src_image_shape (Tuple[int, int]): A (height, width) tuple of the large
+ image's width and height.
+ nms_cfg (dict): it should specify nms type and other parameters
+ like `iou_threshold`.
+ Returns:
+ :obj:`DetDataSample`: merged results.
+ """
+ shifted_instances = shift_predictions(results, offsets, src_image_shape)
+
+ _, keeps = batched_nms(
+ boxes=shifted_instances.bboxes,
+ scores=shifted_instances.scores,
+ idxs=shifted_instances.labels,
+ nms_cfg=nms_cfg)
+ merged_instances = shifted_instances[keeps]
+
+ merged_result = results[0].clone()
+ merged_result.pred_instances = merged_instances
+ return merged_result
diff --git a/requirements/sahi.txt b/requirements/sahi.txt
new file mode 100644
index 00000000..0e7b7b84
--- /dev/null
+++ b/requirements/sahi.txt
@@ -0,0 +1 @@
+sahi>=0.11.4