From 9aaa5d5ed0e5a0c1f053069dd73f12b845c4f282 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 29 Sep 2023 21:06:14 +0200 Subject: [PATCH] `ultralytics 8.0.190` add `thop>=0.1.1` to requirements.txt (#5162) Co-authored-by: JohanWesto Co-authored-by: Muhammad Rizwan Munawar <62513924+RizwanMunawar@users.noreply.github.com> Co-authored-by: StephenBeirlaen <11806615+StephenBeirlaen@users.noreply.github.com> --- docs/modes/predict.md | 7 +++++++ docs/modes/track.md | 9 ++++++++- requirements.txt | 2 +- ultralytics/__init__.py | 2 +- ultralytics/data/augment.py | 14 +++++++------- ultralytics/data/dataset.py | 2 +- ultralytics/engine/exporter.py | 3 +++ 7 files changed, 28 insertions(+), 11 deletions(-) diff --git a/docs/modes/predict.md b/docs/modes/predict.md index eb405534..4c7175b1 100644 --- a/docs/modes/predict.md +++ b/docs/modes/predict.md @@ -23,6 +23,13 @@ In the world of machine learning and computer vision, the process of making sens Watch: How to Extract the Outputs from Ultralytics YOLOv8 Model for Custom Projects.

+## Real-world Applications + +| Manufacturing | Sports | Safety | +|:-----------------------------------:|:-----------------------:|:-----------:| +| ![Vehicle Spare Parts Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1) | ![Football Player Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442)| ![People Fall Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43) | +| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection | + ## Why Use Ultralytics YOLO for Inference? Here's why you should consider YOLOv8's predict mode for your various inference needs: diff --git a/docs/modes/track.md b/docs/modes/track.md index 43bd522c..878ddffe 100644 --- a/docs/modes/track.md +++ b/docs/modes/track.md @@ -30,6 +30,13 @@ The output from Ultralytics trackers is consistent with standard object detectio Watch: Object Detection and Tracking with Ultralytics YOLOv8.

+## Real-world Applications + +| Transportation | Retail | Aquaculture | +|:-----------------------------------:|:-----------------------:|:-----------:| +| ![Vehicle Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab) | ![People Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527) | ![Fish Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142) | +| Vehicle Tracking | People Tracking | Fish Tracking | + ## Features at a Glance Ultralytics YOLO extends its object detection features to provide robust and versatile object tracking: @@ -321,4 +328,4 @@ By contributing to this section, you help expand the scope of tracking solutions To initiate your contribution, please refer to our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for comprehensive instructions on submitting a Pull Request (PR) 🛠️. We are excited to see what you bring to the table! -Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏! \ No newline at end of file +Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏! diff --git a/requirements.txt b/requirements.txt index b2885188..8a5f36d6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -38,7 +38,7 @@ seaborn>=0.11.0 # Extras -------------------------------------- psutil # system utilization py-cpuinfo # display CPU info -# thop>=0.1.1 # FLOPs computation +thop>=0.1.1 # FLOPs computation # ipython # interactive notebook # albumentations>=1.0.3 # training augmentations # pycocotools>=2.0.6 # COCO mAP diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 7d5ce312..9ea9447d 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -__version__ = '8.0.189' +__version__ = '8.0.190' from ultralytics.models import RTDETR, SAM, YOLO from ultralytics.models.fastsam import FastSAM diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py index 3136adba..432023c7 100644 --- a/ultralytics/data/augment.py +++ b/ultralytics/data/augment.py @@ -791,14 +791,14 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False): # Classification augmentations ----------------------------------------------------------------------------------------- -def classify_transforms(size=224, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD - # Transforms to apply if albumentations not installed +def classify_transforms(size=224, rect=False, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD + """Transforms to apply if albumentations not installed.""" if not isinstance(size, int): raise TypeError(f'classify_transforms() size {size} must be integer, not (list, tuple)') + transforms = [ClassifyLetterBox(size, auto=True) if rect else CenterCrop(size), ToTensor()] if any(mean) or any(std): - return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(mean, std, inplace=True)]) - else: - return T.Compose([CenterCrop(size), ToTensor()]) + transforms.append(T.Normalize(mean, std, inplace=True)) + return T.Compose(transforms) def hsv2colorjitter(h, s, v): @@ -864,9 +864,9 @@ class ClassifyLetterBox: imh, imw = im.shape[:2] r = min(self.h / imh, self.w / imw) # ratio of new/old h, w = round(imh * r), round(imw * r) # resized image - hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w + hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else (self.h, self.w) top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) - im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) + im_out = np.full((hs, ws, 3), 114, dtype=im.dtype) im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) return im_out diff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py index 65fe141b..217d3bab 100644 --- a/ultralytics/data/dataset.py +++ b/ultralytics/data/dataset.py @@ -222,7 +222,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder): self.cache_disk = cache == 'disk' self.samples = self.verify_images() # filter out bad images self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im - self.torch_transforms = classify_transforms(args.imgsz) + self.torch_transforms = classify_transforms(args.imgsz, rect=args.rect) self.album_transforms = classify_albumentations( augment=augment, size=args.imgsz, diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py index e03e5207..b209b9d7 100644 --- a/ultralytics/engine/exporter.py +++ b/ultralytics/engine/exporter.py @@ -627,6 +627,9 @@ class Exporter: if builder.platform_has_fast_fp16 and self.args.half: config.set_flag(trt.BuilderFlag.FP16) + del self.model + torch.cuda.empty_cache() + # Write file with builder.build_engine(network, config) as engine, open(f, 'wb') as t: # Metadata