Chiebot-Mirror
/
ultralytics
mirror of https://github.com/ultralytics/ultralytics.git
9
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into quan

Browse Source
test-quan
Francesco Mattioli 1 month ago committed by GitHub
parent ccb5051f7f f4e7756bff
commit eb4642abe8
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
9 changed files with 47 additions and 20 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 7
      docs/en/models/sam-2.md
  2. 7
      docs/en/models/sam.md
  3. 2
      examples/README.md
  4. 12
      tests/test_exports.py
  5. 2
      ultralytics/__init__.py
  6. 9
      ultralytics/data/annotator.py
  7. 7
      ultralytics/engine/trainer.py
  8. 3
      ultralytics/nn/autobackend.py
  9. 18
      ultralytics/nn/modules/head.py

7
docs/en/models/sam-2.md
Unescape View File

@ -250,15 +250,18 @@ To auto-annotate your dataset using SAM 2, follow this example:
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam2_b.pt")
auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam2_b.pt")
```
| Argument | Type | Description | Default |
| ------------ | ----------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
| `data` | `str` | Path to a folder containing images to be annotated. | |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | `'yolov8x.pt'` |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'. | `'yolo11x.pt'` |
| `sam_model` | `str`, optional | Pre-trained SAM 2 segmentation model. Defaults to 'sam2_b.pt'. | `'sam2_b.pt'` |
| `device` | `str`, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | |
| `conf` | `float`, optional | Confidence threshold for detection model; default is 0.25. | `0.25` |
| `iou` | `float`, optional | IoU threshold for filtering overlapping boxes in detection results; default is 0.45. | `0.45` |
| `imgsz` | `int`, optional | Input image resize dimension; default is 640. | `640` |
| `output_dir` | `str`, `None`, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None` |
This function facilitates the rapid creation of high-quality segmentation datasets, ideal for researchers and developers aiming to accelerate their projects.

7
docs/en/models/sam.md
Unescape View File

@ -205,15 +205,18 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt")
```
| Argument | Type | Description | Default |
| ------------ | --------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
| `data` | `str` | Path to a folder containing images to be annotated. | |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | `'yolov8x.pt'` |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'. | `'yolo11x.pt'` |
| `sam_model` | `str`, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | `'sam_b.pt'` |
| `device` | `str`, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | |
| `conf` | `float`, optional | Confidence threshold for detection model; default is 0.25. | `0.25` |
| `iou` | `float`, optional | IoU threshold for filtering overlapping boxes in detection results; default is 0.45. | `0.45` |
| `imgsz` | `int`, optional | Input image resize dimension; default is 640. | `640` |
| `output_dir` | `str`, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None` |
The `auto_annotate` function takes the path to your images, with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.

2
examples/README.md
Unescape View File

@ -8,7 +8,7 @@ This directory features a collection of real-world applications and walkthroughs
| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------- |
| [YOLO ONNX Detection Inference with C++](./YOLOv8-CPP-Inference) | C++/ONNX | [Justas Bartnykas](https://github.com/JustasBart) |
| [YOLO OpenCV ONNX Detection Python](./YOLOv8-OpenCV-ONNX-Python) | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
| [YOLOv8 .NET ONNX ImageSharp](https://github.com/dme-compunet/YOLOv8) | C#/ONNX/ImageSharp | [Compunet](https://github.com/dme-compunet) |
| [YOLO C# ONNX-Runtime](https://github.com/dme-compunet/YoloSharp) | .NET/ONNX-Runtime | [Compunet](https://github.com/dme-compunet) |
| [YOLO .Net ONNX Detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net | [Samuel Stainback](https://github.com/sstainba) |
| [YOLOv8 on NVIDIA Jetson(TensorRT and DeepStream)](https://wiki.seeedstudio.com/YOLOv8-DeepStream-TRT-Jetson/) | Python | [Lakshantha](https://github.com/lakshanthad) |
| [YOLOv8 ONNXRuntime Python](./YOLOv8-ONNXRuntime) | Python/ONNXRuntime | [Semih Demirel](https://github.com/semihhdemirel) |

12
tests/test_exports.py
Unescape View File

@ -193,16 +193,16 @@ def test_export_paddle():
@pytest.mark.slow
def test_export_ncnn():
"""Test YOLO exports to NCNN format."""
file = YOLO(MODEL).export(format="ncnn", imgsz=32)
def test_export_mnn():
"""Test YOLO exports to MNN format (WARNING: MNN test must precede NCNN test or CI error on Windows)."""
file = YOLO(MODEL).export(format="mnn", imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference
@pytest.mark.slow
def test_export_mnn():
"""Test YOLO exports to MNN format."""
file = YOLO(MODEL).export(format="mnn", imgsz=32)
def test_export_ncnn():
"""Test YOLO exports to NCNN format."""
file = YOLO(MODEL).export(format="ncnn", imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference

2
ultralytics/__init__.py
Unescape View File

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = "8.3.25"
__version__ = "8.3.26"
import os

9
ultralytics/data/annotator.py
Unescape View File

@ -5,7 +5,9 @@ from pathlib import Path
from ultralytics import SAM, YOLO
def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="", output_dir=None):
def auto_annotate(
data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", conf=0.25, iou=0.45, imgsz=640, output_dir=None
):
"""
Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
@ -17,6 +19,9 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
det_model (str): Path or name of the pre-trained YOLO detection model.
sam_model (str): Path or name of the pre-trained SAM segmentation model.
device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
conf (float): Confidence threshold for detection model; default is 0.25.
iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
imgsz (int): Input image resize dimension; default is 640.
output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
Examples:
@ -36,7 +41,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
Path(output_dir).mkdir(exist_ok=True, parents=True)
det_results = det_model(data, stream=True, device=device)
det_results = det_model(data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz)
for result in det_results:
class_ids = result.boxes.cls.int().tolist() # noqa

7
ultralytics/engine/trainer.py
Unescape View File

@ -791,6 +791,8 @@ class BaseTrainer:
else: # weight (with decay)
g[0].append(param)
optimizers = {"Adam", "Adamax", "AdamW", "NAdam", "RAdam", "RMSProp", "SGD", "auto"}
name = {x.lower(): x for x in optimizers}.get(name.lower(), None)
if name in {"Adam", "Adamax", "AdamW", "NAdam", "RAdam"}:
optimizer = getattr(optim, name, optim.Adam)(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
elif name == "RMSProp":
@ -799,9 +801,8 @@ class BaseTrainer:
optimizer = optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
else:
raise NotImplementedError(
f"Optimizer '{name}' not found in list of available optimizers "
f"[Adam, AdamW, NAdam, RAdam, RMSProp, SGD, auto]."
"To request support for addition optimizers please visit https://github.com/ultralytics/ultralytics."
f"Optimizer '{name}' not found in list of available optimizers {optimizers}. "
"Request support for addition optimizers at https://github.com/ultralytics/ultralytics."
)
optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay

3
ultralytics/nn/autobackend.py
Unescape View File

@ -684,6 +684,9 @@ class AutoBackend(nn.Module):
else:
x[:, [0, 2]] *= w
x[:, [1, 3]] *= h
if self.task == "pose":
x[:, 5::3] *= w
x[:, 6::3] *= h
y.append(x)
# TF segment fixes: export is reversed vs ONNX export and protos are transposed
if len(y) == 2: # segment with (det, proto) output order reversed

18
ultralytics/nn/modules/head.py
Unescape View File

@ -252,9 +252,21 @@ class Pose(Detect):
def kpts_decode(self, bs, kpts):
"""Decodes keypoints."""
ndim = self.kpt_shape[1]
if self.export: # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
y = kpts.view(bs, *self.kpt_shape, -1)
a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
if self.export:
if self.format in {
"tflite",
"edgetpu",
}: # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
# Precompute normalization factor to increase numerical stability
y = kpts.view(bs, *self.kpt_shape, -1)
grid_h, grid_w = self.shape[2], self.shape[3]
grid_size = torch.tensor([grid_w, grid_h], device=y.device).reshape(1, 2, 1)
norm = self.strides / (self.stride[0] * grid_size)
a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * norm
else:
# NCNN fix
y = kpts.view(bs, *self.kpt_shape, -1)
a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
if ndim == 3:
a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
return a.view(bs, self.nk, -1)

Write Preview
Loading…
Cancel
Save