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ultralytics 8.0.196 instance-mean Segment loss (#5285)

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Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-09 20:08:39 +02:00 • committed by GitHub
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4
docs/models/fast-sam.md
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@ -55,7 +55,7 @@ To perform object detection on an image, use the `predict` method as shown below
# Run inference on an image
everything_results = model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
# Prepare a Prompt Process object
prompt_process = FastSAMPrompt(source, everything_results, device='cpu')
@ -74,7 +74,7 @@ To perform object detection on an image, use the `predict` method as shown below
ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
prompt_process.plot(annotations=ann, output='./')
```
=== "CLI"
```bash
# Load a FastSAM model and segment everything with it

8
docs/models/mobile-sam.md
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@ -66,10 +66,10 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
=== "Python"
```python
from ultralytics import SAM
# Load the model
model = SAM('mobile_sam.pt')
# Predict a segment based on a point prompt
model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
```
@ -81,10 +81,10 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
=== "Python"
```python
from ultralytics import SAM
# Load the model
model = SAM('mobile_sam.pt')
# Predict a segment based on a box prompt
model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
```

2
docs/models/rtdetr.md
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@ -54,7 +54,7 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
=== "CLI"
```bash
```bash
# Load a COCO-pretrained RT-DETR-l model and train it on the COCO8 example dataset for 100 epochs
yolo train model=rtdetr-l.pt data=coco8.yaml epochs=100 imgsz=640

11
docs/models/sam.md
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@ -152,28 +152,27 @@ This comparison shows the order-of-magnitude differences in the model sizes and
Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
!!! example ""
=== "Python"
```python
from ultralytics import FastSAM, SAM, YOLO
# Profile SAM-b
model = SAM('sam_b.pt')
model.info()
model('ultralytics/assets')
# Profile MobileSAM
model = SAM('mobile_sam.pt')
model.info()
model('ultralytics/assets')
# Profile FastSAM-s
model = FastSAM('FastSAM-s.pt')
model.info()
model('ultralytics/assets')
# Profile YOLOv8n-seg
model = YOLO('yolov8n-seg.pt')
model.info()
@ -193,7 +192,7 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
=== "Python"
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```

3
docs/models/yolov6.md
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@ -12,8 +12,7 @@ keywords: Meituan YOLOv6, object detection, Ultralytics, YOLOv6 docs, Bi-directi
![Meituan YOLOv6](https://user-images.githubusercontent.com/26833433/240750495-4da954ce-8b3b-41c4-8afd-ddb74361d3c2.png)
![Model example image](https://user-images.githubusercontent.com/26833433/240750557-3e9ec4f0-0598-49a8-83ea-f33c91eb6d68.png)
**Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The
structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
**Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
### Key Features

2
docs/models/yolov8.md
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@ -51,7 +51,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
=== "Detection (Open Images V7)"
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/), which include 600 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |