ultralytics 8.2.62 add Explorer CLI model and data args (#14581)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
f4af1bccc6
commit
3b81b95e1c
8 changed files with 153 additions and 127 deletions
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@ -192,7 +192,7 @@ def convert_plaintext_links_to_html(content):
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for paragraph in main_content.find_all(["p", "li"]): # Focus on paragraphs and list items
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for text_node in paragraph.find_all(string=True, recursive=False):
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if text_node.parent.name not in {"a", "code"}: # Ignore links and code blocks
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new_text = re.sub(r"(https?://\S+)", r'<a href="\1">\1</a>', str(text_node)) # note: reject http?
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new_text = re.sub(r"(https?://\S+?)(?=[,.!?;:]?\s|[,.!?;:]?$)", r'<a href="\1">\1</a>', str(text_node))
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if "<a" in new_text:
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new_soup = BeautifulSoup(new_text, "html.parser")
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text_node.replace_with(new_soup)
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@ -34,6 +34,7 @@ muhammadrizwanmunawar123@gmail.com: RizwanMunawar
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not.committed.yet: null
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plashchynski@gmail.com: plashchynski
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priytosh.revolution@live.com: priytosh-tripathi
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rulosanti@gmail.com: null
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shuizhuyuanluo@126.com: null
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stormsson@users.noreply.github.com: stormsson
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waxmann.sergiu@me.com: sergiuwaxmann
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.2.61"
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__version__ = "8.2.62"
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import os
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@ -79,7 +79,7 @@ CLI_HELP_MSG = f"""
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
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5. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API
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yolo explorer
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yolo explorer data=data.yaml model=yolov8n.pt
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6. Streamlit real-time object detection on your webcam with Ultralytics YOLOv8
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yolo streamlit-predict
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@ -233,7 +233,7 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
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(SimpleNamespace): Namespace containing the merged configuration arguments.
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Examples:
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>>> from ultralytics import get_cfg
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>>> from ultralytics.cfg import get_cfg
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>>> config = get_cfg() # Load default configuration
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>>> config = get_cfg('path/to/config.yaml', overrides={'epochs': 50, 'batch_size': 16})
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@ -546,16 +546,19 @@ def handle_yolo_settings(args: List[str]) -> None:
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LOGGER.warning(f"WARNING ⚠️ settings error: '{e}'. Please see {url} for help.")
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def handle_explorer():
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def handle_explorer(args: List[str]):
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"""
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Open the Ultralytics Explorer GUI for dataset exploration and analysis.
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This function launches a graphical user interface that provides tools for interacting with and analyzing datasets
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using the Ultralytics Explorer API. It checks for the required 'streamlit' package and informs the user that the
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Explorer dashboard is loading.
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This function launches a graphical user interface that provides tools for interacting with and analyzing
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datasets using the Ultralytics Explorer API. It checks for the required 'streamlit' package and informs
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the user that the Explorer dashboard is loading.
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Args:
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args (List[str]): A list of optional command line arguments.
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Examples:
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>>> handle_explorer()
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```bash
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yolo explorer data=data.yaml model=yolov8n.pt
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```
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Notes:
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- Requires 'streamlit' package version 1.29.0 or higher.
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@ -564,7 +567,12 @@ def handle_explorer():
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"""
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checks.check_requirements("streamlit>=1.29.0")
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LOGGER.info("💡 Loading Explorer dashboard...")
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subprocess.run(["streamlit", "run", ROOT / "data/explorer/gui/dash.py", "--server.maxMessageSize", "2048"])
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cmd = ["streamlit", "run", ROOT / "data/explorer/gui/dash.py", "--server.maxMessageSize", "2048"]
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new = dict(parse_key_value_pair(a) for a in args)
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check_dict_alignment(base={k: DEFAULT_CFG_DICT[k] for k in ["model", "data"]}, custom=new)
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for k, v in new.items():
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cmd += [k, v]
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subprocess.run(cmd)
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def handle_streamlit_inference():
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@ -587,7 +595,7 @@ def handle_streamlit_inference():
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subprocess.run(["streamlit", "run", ROOT / "solutions/streamlit_inference.py", "--server.headless", "true"])
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def parse_key_value_pair(pair):
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def parse_key_value_pair(pair: str = "key=value"):
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"""
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Parses a key-value pair string into separate key and value components.
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@ -650,7 +658,7 @@ def smart_value(v):
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Notes:
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- The function uses a case-insensitive comparison for boolean and None values.
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- For other types, it attempts to use Python's eval() function, which can be unsafe if used with untrusted input.
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- For other types, it attempts to use Python's eval() function, which can be unsafe if used on untrusted input.
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- If no conversion is possible, the original string is returned.
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"""
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v_lower = v.lower()
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@ -705,7 +713,7 @@ def entrypoint(debug=""):
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"hub": lambda: handle_yolo_hub(args[1:]),
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"login": lambda: handle_yolo_hub(args),
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"copy-cfg": copy_default_cfg,
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"explorer": lambda: handle_explorer(),
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"explorer": lambda: handle_explorer(args[1:]),
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"streamlit-predict": lambda: handle_streamlit_inference(),
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}
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full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
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@ -9,20 +9,24 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
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"""
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Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
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This function processes images in a specified directory, detects objects using a YOLO model, and then generates
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segmentation masks using a SAM model. The resulting annotations are saved as text files.
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Args:
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data (str): Path to a folder containing images to be annotated.
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det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.
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sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.
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device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
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output_dir (str | None | optional): Directory to save the annotated results.
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Defaults to a 'labels' folder in the same directory as 'data'.
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det_model (str): Path or name of the pre-trained YOLO detection model.
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sam_model (str): Path or name of the pre-trained SAM segmentation model.
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device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
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output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
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Example:
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```python
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from ultralytics.data.annotator import auto_annotate
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Examples:
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>>> from ultralytics.data.annotator import auto_annotate
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>>> auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
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auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
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```
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Notes:
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- The function creates a new directory for output if not specified.
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- Annotation results are saved as text files with the same names as the input images.
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- Each line in the output text file represents a detected object with its class ID and segmentation points.
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"""
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det_model = YOLO(det_model)
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sam_model = SAM(sam_model)
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@ -159,11 +159,11 @@ class Compose:
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tolist: Converts the list of transforms to a standard Python list.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(30), RandomCrop((224, 224))]
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>>> transforms = [RandomFlip(), RandomPerspective(30)]
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>>> compose = Compose(transforms)
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>>> transformed_data = compose(data)
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>>> compose.append(Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
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>>> compose.insert(0, Resize((256, 256)))
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>>> compose.append(CenterCrop((224, 224)))
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>>> compose.insert(0, RandomFlip())
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"""
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def __init__(self, transforms):
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@ -174,8 +174,8 @@ class Compose:
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transforms (List[Callable]): A list of callable transform objects to be applied sequentially.
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Examples:
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>>> from ultralytics.data.augment import Compose, Resize, RandomFlip
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>>> transforms = [Resize(640), RandomFlip()]
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>>> from ultralytics.data.augment import Compose, RandomHSV, RandomFlip
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>>> transforms = [RandomHSV(), RandomFlip()]
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>>> compose = Compose(transforms)
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"""
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self.transforms = transforms if isinstance(transforms, list) else [transforms]
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@ -209,7 +209,7 @@ class Compose:
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transform (BaseTransform): The transformation to be added to the composition.
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Examples:
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>>> compose = Compose([RandomFlip(), RandomRotate()])
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>>> compose = Compose([RandomFlip(), RandomPerspective()])
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>>> compose.append(RandomHSV())
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"""
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self.transforms.append(transform)
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@ -232,7 +232,7 @@ class Compose:
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def __getitem__(self, index: Union[list, int]) -> "Compose":
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"""
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Retrieve a specific transform or a set of transforms using indexing.
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Retrieves a specific transform or a set of transforms using indexing.
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Args:
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index (int | List[int]): Index or list of indices of the transforms to retrieve.
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@ -244,10 +244,10 @@ class Compose:
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AssertionError: If the index is not of type int or list.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(10), RandomHSV(0.5, 0.5, 0.5)]
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>>> transforms = [RandomFlip(), RandomPerspective(10), RandomHSV(0.5, 0.5, 0.5)]
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>>> compose = Compose(transforms)
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>>> single_transform = compose[1] # Returns a Compose object with only RandomRotate
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>>> multiple_transforms = compose[0:2] # Returns a Compose object with RandomFlip and RandomRotate
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>>> single_transform = compose[1] # Returns a Compose object with only RandomPerspective
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>>> multiple_transforms = compose[0:2] # Returns a Compose object with RandomFlip and RandomPerspective
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"""
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assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
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index = [index] if isinstance(index, int) else index
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@ -288,7 +288,7 @@ class Compose:
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(List): A list containing all the transform objects in the Compose instance.
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Examples:
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>>> transforms = [RandomFlip(), RandomRotate(10), RandomCrop()]
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>>> transforms = [RandomFlip(), RandomPerspective(10), CenterCrop()]
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>>> compose = Compose(transforms)
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>>> transform_list = compose.tolist()
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>>> print(len(transform_list))
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@ -304,12 +304,12 @@ class Compose:
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(str): A string representation of the Compose object, including the list of transforms.
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Examples:
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>>> transforms = [RandomFlip(), RandomAffine(degrees=10, translate=0.1, scale=0.1)]
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>>> transforms = [RandomFlip(), RandomPerspective(degrees=10, translate=0.1, scale=0.1)]
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>>> compose = Compose(transforms)
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>>> print(compose)
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Compose([
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RandomFlip(),
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RandomAffine(degrees=10, translate=0.1, scale=0.1)
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RandomPerspective(degrees=10, translate=0.1, scale=0.1)
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])
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"""
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return f"{self.__class__.__name__}({', '.join([f'{t}' for t in self.transforms])})"
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@ -353,12 +353,12 @@ class BaseMixTransform:
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Args:
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dataset (Any): The dataset object containing images and labels for mixing.
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pre_transform (Callable | None): Optional transform to apply before mixing. If None, no pre-transform is applied.
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pre_transform (Callable | None): Optional transform to apply before mixing.
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p (float): Probability of applying the mix transformation. Should be in the range [0.0, 1.0].
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Examples:
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>>> dataset = YOLODataset("path/to/data")
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>>> pre_transform = Compose([RandomFlip(), RandomRotate()])
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>>> pre_transform = Compose([RandomFlip(), RandomPerspective()])
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>>> mix_transform = BaseMixTransform(dataset, pre_transform, p=0.5)
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"""
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self.dataset = dataset
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@ -420,7 +420,7 @@ class BaseMixTransform:
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(Dict): The modified labels dictionary with augmented data after applying the mix transform.
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Examples:
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>>> transform = MixUpTransform(dataset)
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>>> transform = BaseMixTransform(dataset)
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>>> labels = {'image': img, 'bboxes': boxes, 'mix_labels': [{'image': img2, 'bboxes': boxes2}]}
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>>> augmented_labels = transform._mix_transform(labels)
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"""
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@ -662,13 +662,12 @@ class Mosaic(BaseMixTransform):
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updates the corresponding labels for each image in the mosaic.
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Args:
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labels (Dict): A dictionary containing image data and labels for the base image (index 0) and
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three additional images (indices 1-3) in the 'mix_labels' key.
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labels (Dict): A dictionary containing image data and labels for the base image (index 0) and three
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additional images (indices 1-3) in the 'mix_labels' key.
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Returns:
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(Dict): A dictionary containing the mosaic image and updated labels. The 'img' key contains the
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mosaic image as a numpy array, and other keys contain the combined and adjusted labels for
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all four images.
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(Dict): A dictionary containing the mosaic image and updated labels. The 'img' key contains the mosaic
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image as a numpy array, and other keys contain the combined and adjusted labels for all four images.
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Examples:
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>>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
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@ -963,7 +962,7 @@ class RandomPerspective:
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shear (float): Maximum shear angle in degrees.
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perspective (float): Perspective distortion factor.
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border (Tuple[int, int]): Mosaic border size as (x, y).
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pre_transform (callable): Optional transform to apply before the random perspective.
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pre_transform (Callable | None): Optional transform to apply before the random perspective.
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Methods:
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affine_transform: Applies affine transformations to the input image.
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@ -988,9 +987,8 @@ class RandomPerspective:
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"""
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Initializes RandomPerspective object with transformation parameters.
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This class implements random perspective and affine transformations on images and corresponding
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bounding boxes, segments, and keypoints. Transformations include rotation, translation, scaling,
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and shearing.
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This class implements random perspective and affine transformations on images and corresponding bounding boxes,
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segments, and keypoints. Transformations include rotation, translation, scaling, and shearing.
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Args:
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degrees (float): Degree range for random rotations.
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@ -999,8 +997,8 @@ class RandomPerspective:
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shear (float): Shear intensity (angle in degrees).
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perspective (float): Perspective distortion factor.
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border (Tuple[int, int]): Tuple specifying mosaic border (top/bottom, left/right).
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pre_transform (Callable | None): Function/transform to apply to the image before starting the
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random transformation.
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pre_transform (Callable | None): Function/transform to apply to the image before starting the random
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transformation.
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Examples:
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>>> transform = RandomPerspective(degrees=10.0, translate=0.1, scale=0.5, shear=5.0)
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@ -1121,8 +1119,8 @@ class RandomPerspective:
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the transformed segments. It clips the transformed segments to fit within the new bounding boxes.
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Args:
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segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M
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is the number of points in each segment.
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segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M is the
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number of points in each segment.
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M (np.ndarray): Affine transformation matrix with shape (3, 3).
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Returns:
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@ -1203,10 +1201,10 @@ class RandomPerspective:
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Returns:
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(Dict): Transformed labels dictionary containing:
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'img' (ndarray): The transformed image.
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'cls' (ndarray): Updated class labels.
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'instances' (Instances): Updated object instances.
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'resized_shape' (Tuple[int, int]): New image shape after transformation.
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- 'img' (np.ndarray): The transformed image.
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- 'cls' (np.ndarray): Updated class labels.
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- 'instances' (Instances): Updated object instances.
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- 'resized_shape' (Tuple[int, int]): New image shape after transformation.
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Examples:
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>>> transform = RandomPerspective()
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@ -1271,9 +1269,9 @@ class RandomPerspective:
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been overly distorted or reduced by the augmentation process.
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Args:
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box1 (numpy.ndarray): Original boxes before augmentation, shape (4, n) where n is the
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box1 (numpy.ndarray): Original boxes before augmentation, shape (4, N) where n is the
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number of boxes. Format is [x1, y1, x2, y2] in absolute coordinates.
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box2 (numpy.ndarray): Augmented boxes after transformation, shape (4, n). Format is
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box2 (numpy.ndarray): Augmented boxes after transformation, shape (4, N). Format is
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[x1, y1, x2, y2] in absolute coordinates.
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wh_thr (float): Width and height threshold in pixels. Boxes smaller than this in either
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dimension are rejected.
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@ -1411,9 +1409,8 @@ class RandomFlip:
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It also updates any instances (bounding boxes, keypoints, etc.) accordingly.
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Args:
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p (float): The probability of applying the flip. Must be between 0 and 1. Default is 0.5.
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p (float): The probability of applying the flip. Must be between 0 and 1.
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direction (str): The direction to apply the flip. Must be 'horizontal' or 'vertical'.
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Default is 'horizontal'.
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flip_idx (List[int] | None): Index mapping for flipping keypoints, if any.
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Raises:
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@ -1538,15 +1535,15 @@ class LetterBox:
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"""
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Resizes and pads an image for object detection, instance segmentation, or pose estimation tasks.
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This method applies letterboxing to the input image, which involves resizing the image while maintaining its aspect
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ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.
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This method applies letterboxing to the input image, which involves resizing the image while maintaining its
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aspect ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.
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Args:
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labels (dict | None): A dictionary containing image data and associated labels. If None, an empty dict is used.
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image (numpy.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.
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labels (Dict | None): A dictionary containing image data and associated labels, or empty dict if None.
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image (np.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.
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Returns:
|
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(dict | tuple): If 'labels' is provided, returns an updated dictionary with the resized and padded image,
|
||||
(Dict | Tuple): If 'labels' is provided, returns an updated dictionary with the resized and padded image,
|
||||
updated labels, and additional metadata. If 'labels' is empty, returns a tuple containing the resized
|
||||
and padded image, and a tuple of (ratio, (left_pad, top_pad)).
|
||||
|
||||
|
|
@ -1675,17 +1672,16 @@ class CopyPaste:
|
|||
Applies Copy-Paste augmentation to an image and its instances.
|
||||
|
||||
Args:
|
||||
labels (dict): A dictionary containing:
|
||||
- 'img' (numpy.ndarray): The image to augment.
|
||||
- 'cls' (numpy.ndarray): Class labels for the instances.
|
||||
labels (Dict): A dictionary containing:
|
||||
- 'img' (np.ndarray): The image to augment.
|
||||
- 'cls' (np.ndarray): Class labels for the instances.
|
||||
- 'instances' (ultralytics.engine.results.Instances): Object containing bounding boxes, segments, etc.
|
||||
|
||||
Returns:
|
||||
(dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
|
||||
(Dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
|
||||
|
||||
Examples:
|
||||
>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1, 2]),
|
||||
... 'instances': Instances(...)}
|
||||
>>> labels = {'img': np.random.rand(640, 640, 3), 'cls': np.array([0, 1, 2]), 'instances': Instances(...)}
|
||||
>>> augmenter = CopyPaste(p=0.5)
|
||||
>>> augmented_labels = augmenter(labels)
|
||||
"""
|
||||
|
|
@ -1874,8 +1870,12 @@ class Albumentations:
|
|||
|
||||
Examples:
|
||||
>>> transform = Albumentations(p=0.5)
|
||||
>>> augmented = transform({"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1]),
|
||||
... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]]))})
|
||||
>>> labels = {
|
||||
... "img": np.random.rand(640, 640, 3),
|
||||
... "cls": np.array([0, 1]),
|
||||
... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]]))
|
||||
... }
|
||||
>>> augmented = transform(labels)
|
||||
>>> assert augmented["img"].shape == (640, 640, 3)
|
||||
|
||||
Notes:
|
||||
|
|
@ -1974,6 +1974,7 @@ class Format:
|
|||
mask_ratio (int): Downsample ratio for masks.
|
||||
mask_overlap (bool): Whether masks can overlap.
|
||||
batch_idx (bool): Whether to keep batch indexes.
|
||||
bgr (float): The probability to return BGR images.
|
||||
|
||||
Examples:
|
||||
>>> format = Format(bbox_format='xyxy', return_mask=True, return_keypoint=False)
|
||||
|
|
@ -1994,9 +1995,9 @@ class Format:
|
|||
"""
|
||||
Formats image annotations for object detection, instance segmentation, and pose estimation tasks.
|
||||
|
||||
This method standardizes the image and instance annotations to be used by the `collate_fn` in PyTorch DataLoader.
|
||||
It processes the input labels dictionary, converting annotations to the specified format and applying
|
||||
normalization if required.
|
||||
This method standardizes the image and instance annotations to be used by the `collate_fn` in PyTorch
|
||||
DataLoader. It processes the input labels dictionary, converting annotations to the specified format and
|
||||
applying normalization if required.
|
||||
|
||||
Args:
|
||||
labels (Dict): A dictionary containing image and annotation data with the following keys:
|
||||
|
|
@ -2069,7 +2070,7 @@ class Format:
|
|||
5. Converts the Numpy array to a PyTorch tensor.
|
||||
|
||||
Args:
|
||||
img (ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).
|
||||
img (np.ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Formatted image as a PyTorch tensor with shape (C, H, W).
|
||||
|
|
@ -2130,11 +2131,11 @@ class RandomLoadText:
|
|||
to reflect the sampled texts and can optionally pad the text list to a fixed length.
|
||||
|
||||
Attributes:
|
||||
prompt_format (str): Format string for text prompts. Default is '{}'.
|
||||
neg_samples (Tuple[int, int]): Range for randomly sampling negative texts. Default is (80, 80).
|
||||
max_samples (int): Maximum number of different text samples in one image. Default is 80.
|
||||
padding (bool): Whether to pad texts to max_samples. Default is False.
|
||||
padding_value (str): The text used for padding when padding is True. Default is "".
|
||||
prompt_format (str): Format string for text prompts.
|
||||
neg_samples (Tuple[int, int]): Range for randomly sampling negative texts.
|
||||
max_samples (int): Maximum number of different text samples in one image.
|
||||
padding (bool): Whether to pad texts to max_samples.
|
||||
padding_value (str): The text used for padding when padding is True.
|
||||
|
||||
Methods:
|
||||
__call__: Processes the input labels and returns updated classes and texts.
|
||||
|
|
@ -2268,15 +2269,15 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
|
|||
Args:
|
||||
dataset (Dataset): The dataset object containing image data and annotations.
|
||||
imgsz (int): The target image size for resizing.
|
||||
hyp (dict): A dictionary of hyperparameters controlling various aspects of the transformations.
|
||||
hyp (Dict): A dictionary of hyperparameters controlling various aspects of the transformations.
|
||||
stretch (bool): If True, applies stretching to the image. If False, uses LetterBox resizing.
|
||||
|
||||
Returns:
|
||||
(Compose): A composition of image transformations to be applied to the dataset.
|
||||
|
||||
Examples:
|
||||
>>> from ultralytics.data.dataset import Dataset
|
||||
>>> dataset = Dataset(img_path='path/to/images', imgsz=640)
|
||||
>>> from ultralytics.data.dataset import YOLODataset
|
||||
>>> dataset = YOLODataset(img_path='path/to/images', imgsz=640)
|
||||
>>> hyp = {'mosaic': 1.0, 'copy_paste': 0.5, 'degrees': 10.0, 'translate': 0.2, 'scale': 0.9}
|
||||
>>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
|
||||
>>> augmented_data = transforms(dataset[0])
|
||||
|
|
@ -2332,15 +2333,12 @@ def classify_transforms(
|
|||
center cropping, conversion to tensor, and normalization.
|
||||
|
||||
Args:
|
||||
size (int | tuple): The target size for the transformed image. If an int, it defines the
|
||||
shortest edge. If a tuple, it defines (height, width).
|
||||
mean (tuple): Mean values for each RGB channel used in normalization. Defaults to
|
||||
DEFAULT_MEAN.
|
||||
size (int | tuple): The target size for the transformed image. If an int, it defines the shortest edge. If a
|
||||
tuple, it defines (height, width).
|
||||
mean (tuple): Mean values for each RGB channel used in normalization.
|
||||
std (tuple): Standard deviation values for each RGB channel used in normalization.
|
||||
Defaults to DEFAULT_STD.
|
||||
interpolation (int): Interpolation method for resizing. Defaults to Image.BILINEAR.
|
||||
crop_fraction (float): Fraction of the image to be cropped. Defaults to
|
||||
DEFAULT_CROP_FRACTION.
|
||||
interpolation (int): Interpolation method for resizing.
|
||||
crop_fraction (float): Fraction of the image to be cropped.
|
||||
|
||||
Returns:
|
||||
(torchvision.transforms.Compose): A composition of torchvision transforms.
|
||||
|
|
@ -2482,7 +2480,7 @@ def classify_augmentations(
|
|||
# NOTE: keep this class for backward compatibility
|
||||
class ClassifyLetterBox:
|
||||
"""
|
||||
YOLOv8 LetterBox class for image preprocessing in classification tasks.
|
||||
A class for resizing and padding images for classification tasks.
|
||||
|
||||
This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]).
|
||||
It resizes and pads images to a specified size while maintaining the original aspect ratio.
|
||||
|
|
@ -2512,8 +2510,8 @@ class ClassifyLetterBox:
|
|||
pads images to a specified size while maintaining the original aspect ratio.
|
||||
|
||||
Args:
|
||||
size (Union[int, Tuple[int, int]]): Target size for the letterboxed image. If int, a square image of
|
||||
(size, size) is created. If tuple, it should be (height, width).
|
||||
size (int | Tuple[int, int]): Target size for the letterboxed image. If an int, a square image of
|
||||
(size, size) is created. If a tuple, it should be (height, width).
|
||||
auto (bool): If True, automatically calculates the short side based on stride. Default is False.
|
||||
stride (int): The stride value, used when 'auto' is True. Default is 32.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import sys
|
||||
import time
|
||||
from threading import Thread
|
||||
|
||||
|
|
@ -17,7 +18,8 @@ def _get_explorer():
|
|||
"""Initializes and returns an instance of the Explorer class."""
|
||||
exp = Explorer(data=st.session_state.get("dataset"), model=st.session_state.get("model"))
|
||||
thread = Thread(
|
||||
target=exp.create_embeddings_table, kwargs={"force": st.session_state.get("force_recreate_embeddings")}
|
||||
target=exp.create_embeddings_table,
|
||||
kwargs={"force": st.session_state.get("force_recreate_embeddings"), "split": st.session_state.get("split")},
|
||||
)
|
||||
thread.start()
|
||||
progress_bar = st.progress(0, text="Creating embeddings table...")
|
||||
|
|
@ -29,33 +31,45 @@ def _get_explorer():
|
|||
progress_bar.empty()
|
||||
|
||||
|
||||
def init_explorer_form():
|
||||
def init_explorer_form(data=None, model=None):
|
||||
"""Initializes an Explorer instance and creates embeddings table with progress tracking."""
|
||||
datasets = ROOT / "cfg" / "datasets"
|
||||
ds = [d.name for d in datasets.glob("*.yaml")]
|
||||
models = [
|
||||
"yolov8n.pt",
|
||||
"yolov8s.pt",
|
||||
"yolov8m.pt",
|
||||
"yolov8l.pt",
|
||||
"yolov8x.pt",
|
||||
"yolov8n-seg.pt",
|
||||
"yolov8s-seg.pt",
|
||||
"yolov8m-seg.pt",
|
||||
"yolov8l-seg.pt",
|
||||
"yolov8x-seg.pt",
|
||||
"yolov8n-pose.pt",
|
||||
"yolov8s-pose.pt",
|
||||
"yolov8m-pose.pt",
|
||||
"yolov8l-pose.pt",
|
||||
"yolov8x-pose.pt",
|
||||
]
|
||||
if data is None:
|
||||
datasets = ROOT / "cfg" / "datasets"
|
||||
ds = [d.name for d in datasets.glob("*.yaml")]
|
||||
else:
|
||||
ds = [data]
|
||||
|
||||
if model is None:
|
||||
models = [
|
||||
"yolov8n.pt",
|
||||
"yolov8s.pt",
|
||||
"yolov8m.pt",
|
||||
"yolov8l.pt",
|
||||
"yolov8x.pt",
|
||||
"yolov8n-seg.pt",
|
||||
"yolov8s-seg.pt",
|
||||
"yolov8m-seg.pt",
|
||||
"yolov8l-seg.pt",
|
||||
"yolov8x-seg.pt",
|
||||
"yolov8n-pose.pt",
|
||||
"yolov8s-pose.pt",
|
||||
"yolov8m-pose.pt",
|
||||
"yolov8l-pose.pt",
|
||||
"yolov8x-pose.pt",
|
||||
]
|
||||
else:
|
||||
models = [model]
|
||||
|
||||
splits = ["train", "val", "test"]
|
||||
|
||||
with st.form(key="explorer_init_form"):
|
||||
col1, col2 = st.columns(2)
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
st.selectbox("Select dataset", ds, key="dataset", index=ds.index("coco128.yaml"))
|
||||
st.selectbox("Select dataset", ds, key="dataset")
|
||||
with col2:
|
||||
st.selectbox("Select model", models, key="model")
|
||||
with col3:
|
||||
st.selectbox("Select split", splits, key="split")
|
||||
st.checkbox("Force recreate embeddings", key="force_recreate_embeddings")
|
||||
|
||||
st.form_submit_button("Explore", on_click=_get_explorer)
|
||||
|
|
@ -182,13 +196,13 @@ def utralytics_explorer_docs_callback():
|
|||
st.link_button("Ultrlaytics Explorer API", "https://docs.ultralytics.com/datasets/explorer/")
|
||||
|
||||
|
||||
def layout():
|
||||
def layout(data=None, model=None):
|
||||
"""Resets explorer session variables and provides documentation with a link to API docs."""
|
||||
st.set_page_config(layout="wide", initial_sidebar_state="collapsed")
|
||||
st.markdown("<h1 style='text-align: center;'>Ultralytics Explorer Demo</h1>", unsafe_allow_html=True)
|
||||
|
||||
if st.session_state.get("explorer") is None:
|
||||
init_explorer_form()
|
||||
init_explorer_form(data, model)
|
||||
return
|
||||
|
||||
st.button(":arrow_backward: Select Dataset", on_click=reset_explorer)
|
||||
|
|
@ -264,4 +278,5 @@ def layout():
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
layout()
|
||||
kwargs = dict(zip(sys.argv[1::2], sys.argv[2::2]))
|
||||
layout(**kwargs)
|
||||
|
|
|
|||
|
|
@ -583,7 +583,7 @@ class Model(nn.Module):
|
|||
**kwargs (Any): Additional keyword arguments for configuring the tracking process.
|
||||
|
||||
Returns:
|
||||
(List[ultralytics.engine.results.Results]): A list of tracking results, each encapsulated in a Results object.
|
||||
(List[ultralytics.engine.results.Results]): A list of tracking results, each a Results object.
|
||||
|
||||
Raises:
|
||||
AttributeError: If the predictor does not have registered trackers.
|
||||
|
|
@ -1028,8 +1028,8 @@ class Model(nn.Module):
|
|||
The default callbacks are defined in the 'callbacks.default_callbacks' dictionary, which contains predefined
|
||||
functions for various events in the model's lifecycle, such as on_train_start, on_epoch_end, etc.
|
||||
|
||||
This method is useful when you want to revert to the original set of callbacks after making custom modifications,
|
||||
ensuring consistent behavior across different runs or experiments.
|
||||
This method is useful when you want to revert to the original set of callbacks after making custom
|
||||
modifications, ensuring consistent behavior across different runs or experiments.
|
||||
|
||||
Examples:
|
||||
>>> model = YOLO('yolov8n.pt')
|
||||
|
|
@ -1122,7 +1122,7 @@ class Model(nn.Module):
|
|||
nested dictionaries. Each nested dictionary has keys 'model', 'trainer', 'validator', and
|
||||
'predictor', mapping to their respective class implementations.
|
||||
|
||||
Example:
|
||||
Examples:
|
||||
>>> model = Model()
|
||||
>>> task_map = model.task_map
|
||||
>>> detect_class_map = task_map['detect']
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue