ultralytics 8.0.87 improved Pose models (#2202)
Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Kirolos Atef <keroatef295@gmail.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.86'
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__version__ = '8.0.87'
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from ultralytics.hub import start
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from ultralytics.yolo.engine.model import YOLO
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@ -89,12 +89,12 @@ Available Models:
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| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 49.7 | 79.7 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 59.2 | 85.8 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 63.6 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67.0 | 89.9 | 784.5 | 2.59 | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 68.9 | 90.4 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.5 | 91.3 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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</details>
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@ -1,13 +1,15 @@
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## Tracker
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# Tracker
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### Trackers
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## Supported Trackers
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- [x] ByteTracker
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- [x] BoT-SORT
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### Usage
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## Usage
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python interface:
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### python interface:
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You can use the Python interface to track objects using the YOLO model.
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```python
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from ultralytics import YOLO
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@ -17,17 +19,68 @@ model.track(
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source="video/streams",
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stream=True,
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tracker="botsort.yaml", # or 'bytetrack.yaml'
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...,
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show=True,
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)
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```
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cli:
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You can get the IDs of the tracked objects using the following code:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt")
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for result in model.track(source="video.mp4"):
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print(
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result.boxes.id.cpu().numpy().astype(int)
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) # this will print the IDs of the tracked objects in the frame
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```
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If you want to use the tracker with a folder of images or when you loop on the video frames, you should use the `persist` parameter to tell the model that these frames are related to each other so the IDs will be fixed for the same objects. Otherwise, the IDs will be different in each frame because in each loop, the model creates a new object for tracking, but the `persist` parameter makes it use the same object for tracking.
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```python
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import cv2
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from ultralytics import YOLO
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cap = cv2.VideoCapture("video.mp4")
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model = YOLO("yolov8n.pt")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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results = model.track(frame, persist=True)
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boxes = results[0].boxes.xyxy.cpu().numpy().astype(int)
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ids = results[0].boxes.id.cpu().numpy().astype(int)
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for box, id in zip(boxes, ids):
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cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
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cv2.putText(
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frame,
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f"Id {id}",
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(box[0], box[1]),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(0, 0, 255),
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2,
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)
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cv2.imshow("frame", frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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```
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## Change tracker parameters
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You can change the tracker parameters by eding the `tracker.yaml` file which is located in the ultralytics/tracker/cfg folder.
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## Command Line Interface (CLI)
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You can also use the command line interface to track objects using the YOLO model.
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```bash
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yolo detect track source=... tracker=...
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yolo segment track source=... tracker=...
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yolo pose track source=... tracker=...
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```
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By default, trackers will use the configuration in `ultralytics/tracker/cfg`.
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We also support using a modified tracker config file. Please refer to the tracker config files
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in `ultralytics/tracker/cfg`.
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in `ultralytics/tracker/cfg`.<br>
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@ -69,10 +69,10 @@ CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay',
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'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou') # fractional floats limited to 0.0 - 1.0
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CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
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'line_thickness', 'workspace', 'nbs', 'save_period')
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CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr',
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'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt',
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'save_conf', 'save_crop', 'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms',
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'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
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CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'rect', 'cos_lr', 'overlap_mask', 'val',
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'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', 'save_crop',
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'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
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'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
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def cfg2dict(cfg):
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@ -7,7 +7,7 @@ from pathlib import Path
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import numpy as np
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import torch
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from PIL import Image
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from torch.utils.data import DataLoader, dataloader, distributed
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from torch.utils.data import dataloader, distributed
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from ultralytics.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots,
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LoadStreams, LoadTensor, SourceTypes, autocast_list)
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@ -38,6 +38,12 @@ class InfiniteDataLoader(dataloader.DataLoader):
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for _ in range(len(self)):
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yield next(self.iterator)
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def reset(self):
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"""Reset iterator.
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This is useful when we want to modify settings of dataset while training.
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"""
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self.iterator = self._get_iterator()
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class _RepeatSampler:
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"""
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@ -94,20 +100,17 @@ def build_dataloader(cfg, batch, img_path, data_info, stride=32, rect=False, ran
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workers = cfg.workers if mode == 'train' else cfg.workers * 2
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nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers]) # number of workers
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sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
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loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader # allow attribute updates
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generator = torch.Generator()
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generator.manual_seed(6148914691236517205 + RANK)
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return loader(
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dataset=dataset,
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batch_size=batch,
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shuffle=shuffle and sampler is None,
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num_workers=nw,
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sampler=sampler,
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pin_memory=PIN_MEMORY,
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collate_fn=getattr(dataset, 'collate_fn', None),
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worker_init_fn=seed_worker,
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persistent_workers=(nw > 0) and (loader == DataLoader), # persist workers if using default PyTorch DataLoader
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generator=generator), dataset
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return InfiniteDataLoader(dataset=dataset,
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batch_size=batch,
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shuffle=shuffle and sampler is None,
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num_workers=nw,
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sampler=sampler,
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pin_memory=PIN_MEMORY,
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collate_fn=getattr(dataset, 'collate_fn', None),
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worker_init_fn=seed_worker,
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generator=generator), dataset
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# Build classification
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@ -296,6 +296,7 @@ class BaseTrainer:
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self.train_loader.dataset.mosaic = False
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if hasattr(self.train_loader.dataset, 'close_mosaic'):
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self.train_loader.dataset.close_mosaic(hyp=self.args)
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self.train_loader.reset()
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if RANK in (-1, 0):
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LOGGER.info(self.progress_string())
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