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Release 8.0.4 fixes (#256)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: TechieG <35962141+gokulnath30@users.noreply.github.com> Co-authored-by: Parthiban Marimuthu <66585214+partheee@users.noreply.github.com>
This commit is contained in:
Ayush Chaurasia
GitHub
parent
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commit
216cf2ddb6
48
README.md
48
README.md
@ -99,8 +99,8 @@ results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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success = YOLO("yolov8n.pt").export(format="onnx") # export a model to ONNX format
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```
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/ultralytics/releases).
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/assets/releases).
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### Known Issues / TODOs
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@ -116,18 +116,18 @@ We are still working on several parts of YOLOv8! We aim to have these completed
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All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset, while Classification models are pretrained on the ImageNet dataset.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on first use.
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<details open><summary>Detection</summary>
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 |
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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@ -138,13 +138,13 @@ Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on fi
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<details><summary>Segmentation</summary>
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| --------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 |
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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@ -155,13 +155,13 @@ Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on fi
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<details><summary>Classification</summary>
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| --------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ |
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| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 |
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| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 |
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| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 |
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| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 |
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| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 |
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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@ -95,7 +95,7 @@ results = model("https://ultralytics.com/images/bus.jpg") # 预测图像
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success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX 格式
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```
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[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
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[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
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### 已知问题 / 待办事项
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@ -111,7 +111,7 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
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所有 YOLOv8 的预训练模型都可以在这里找到。目标检测和分割模型是在 COCO 数据集上预训练的，而分类模型是在 ImageNet 数据集上预训练的。
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第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
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第一次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
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<details open><summary>目标检测</summary>
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@ -55,16 +55,16 @@ You can override config file entirely by passing a new file. You can create a co
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```bash
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yolo task=init
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```
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You can then use special `--cfg name.yaml` command to pass the new config file
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You can then use `cfg=name.yaml` command to pass the new config file
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```bash
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yolo task=detect mode=train {++ --cfg default.yaml ++}
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yolo cfg=default.yaml
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```
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??? example
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=== "Command"
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```
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yolo task=init
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yolo task=detect mode=train --cfg default.yaml
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yolo cfg=default.yaml
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```
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=== "Result"
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TODO: add terminal output
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@ -23,7 +23,7 @@ def test_train_seg():
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def test_train_cls():
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os.system(f'yolo mode=train task=classify model={CFG}-cls.yaml data=imagenette160 imgsz=32 epochs=1')
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os.system(f'yolo mode=train task=classify model={CFG}-cls.yaml data=mnist160 imgsz=32 epochs=1')
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# Val checks -----------------------------------------------------------------------------------------------------------
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@ -26,8 +26,10 @@ def test_detect():
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# predictor
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pred = detect.DetectionPredictor(overrides={"imgsz": [640, 640]})
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p = pred(source=SOURCE, model="yolov8n.pt")
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assert len(p) == 2, "predictor test failed"
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i = 0
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for _ in pred(source=SOURCE, model="yolov8n.pt"):
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i += 1
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assert i == 2, "predictor test failed"
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overrides["resume"] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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@ -57,8 +59,10 @@ def test_segment():
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# predictor
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pred = segment.SegmentationPredictor(overrides={"imgsz": [640, 640]})
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p = pred(source=SOURCE, model="yolov8n-seg.pt")
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assert len(p) == 2, "predictor test failed"
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i = 0
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for _ in pred(source=SOURCE, model="yolov8n-seg.pt"):
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i += 1
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assert i == 2, "predictor test failed"
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# test resume
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overrides["resume"] = trainer.last
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@ -73,14 +77,8 @@ def test_segment():
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def test_classify():
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overrides = {
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"data": "imagenette160",
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"model": "yolov8n-cls.yaml",
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"imgsz": 32,
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"epochs": 1,
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"batch": 64,
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"save": False}
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CFG.data = "imagenette160"
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overrides = {"data": "mnist160", "model": "yolov8n-cls.yaml", "imgsz": 32, "epochs": 1, "batch": 64, "save": False}
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CFG.data = "mnist160"
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CFG.imgsz = 32
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CFG.batch = 64
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# YOLO(CFG_SEG).train(**overrides) # This works
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@ -95,5 +93,7 @@ def test_classify():
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# predictor
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pred = classify.ClassificationPredictor(overrides={"imgsz": [640, 640]})
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p = pred(source=SOURCE, model=trained_model)
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assert len(p) == 2, "Predictor test failed!"
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i = 0
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for _ in pred(source=SOURCE, model=trained_model):
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i += 1
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assert i == 2, "predictor test failed"
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@ -32,7 +32,7 @@ def test_model_fuse():
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def test_predict_dir():
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model = YOLO(MODEL)
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model.predict(source=ROOT / "assets")
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model.predict(source=ROOT / "assets", return_outputs=False)
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def test_val():
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@ -56,6 +56,7 @@ class AutoBackend(nn.Module):
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fp16 &= pt or jit or onnx or engine or nn_module # FP16
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nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
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stride = 32 # default stride
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model = None # TODO: resolves ONNX inference, verify effect on other backends
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cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
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if not (pt or triton or nn_module):
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w = attempt_download(w) # download if not local
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@ -6,6 +6,7 @@ from pathlib import Path
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import hydra
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from ultralytics import hub, yolo
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr
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DIR = Path(__file__).parent
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@ -20,6 +21,9 @@ def cli(cfg):
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cfg (DictConfig): Configuration for the task and mode.
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"""
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# LOGGER.info(f"{colorstr(f'Ultralytics YOLO v{ultralytics.__version__}')}")
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if cfg.cfg:
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LOGGER.info(f"Overriding default config with {cfg.cfg}")
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cfg = get_config(cfg.cfg)
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task, mode = cfg.task.lower(), cfg.mode.lower()
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# Special case for initializing the configuration
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@ -28,7 +32,7 @@ def cli(cfg):
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LOGGER.info(f"""
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{colorstr("YOLO:")} configuration saved to {Path.cwd() / DEFAULT_CONFIG.name}.
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To run experiments using custom configuration:
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yolo task='task' mode='mode' --config-name config_file.yaml
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yolo cfg=config_file.yaml
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""")
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return
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@ -101,6 +101,7 @@ mixup: 0.0 # image mixup (probability)
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copy_paste: 0.0 # segment copy-paste (probability)
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# Hydra configs --------------------------------------------------------------------------------------------------------
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cfg: null # for overriding defaults.yaml
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hydra:
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output_subdir: null # disable hydra directory creation
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run:
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@ -111,7 +111,7 @@ class YOLO:
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self.model.fuse()
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@smart_inference_mode()
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def predict(self, source, **kwargs):
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def predict(self, source, return_outputs=True, **kwargs):
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"""
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Visualize prediction.
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@ -127,8 +127,8 @@ class YOLO:
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predictor = self.PredictorClass(overrides=overrides)
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predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size
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predictor.setup(model=self.model, source=source)
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return predictor()
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predictor.setup(model=self.model, source=source, return_outputs=return_outputs)
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return predictor() if return_outputs else predictor.predict_cli()
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@smart_inference_mode()
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def val(self, data=None, **kwargs):
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@ -212,10 +212,12 @@ class YOLO:
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@staticmethod
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def _reset_ckpt_args(args):
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args.pop("device", None)
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args.pop("project", None)
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args.pop("name", None)
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args.pop("batch", None)
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args.pop("epochs", None)
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args.pop("cache", None)
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args.pop("save_json", None)
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# set device to '' to prevent from auto DDP usage
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args["device"] = ''
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@ -89,6 +89,7 @@ class BasePredictor:
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self.vid_path, self.vid_writer = None, None
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self.annotator = None
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self.data_path = None
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self.output = dict()
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self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
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callbacks.add_integration_callbacks(self)
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@ -104,7 +105,7 @@ class BasePredictor:
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def postprocess(self, preds, img, orig_img):
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return preds
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def setup(self, source=None, model=None):
|
||||
def setup(self, source=None, model=None, return_outputs=True):
|
||||
# source
|
||||
source = str(source if source is not None else self.args.source)
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
@ -155,16 +156,16 @@ class BasePredictor:
|
||||
self.imgsz = imgsz
|
||||
self.done_setup = True
|
||||
self.device = device
|
||||
self.return_outputs = return_outputs
|
||||
|
||||
return model
|
||||
|
||||
@smart_inference_mode()
|
||||
def __call__(self, source=None, model=None):
|
||||
def __call__(self, source=None, model=None, return_outputs=True):
|
||||
self.run_callbacks("on_predict_start")
|
||||
model = self.model if self.done_setup else self.setup(source, model)
|
||||
model = self.model if self.done_setup else self.setup(source, model, return_outputs)
|
||||
model.eval()
|
||||
self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
|
||||
self.all_outputs = []
|
||||
for batch in self.dataset:
|
||||
self.run_callbacks("on_predict_batch_start")
|
||||
path, im, im0s, vid_cap, s = batch
|
||||
@ -194,6 +195,10 @@ class BasePredictor:
|
||||
if self.args.save:
|
||||
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
|
||||
|
||||
if self.return_outputs:
|
||||
yield self.output
|
||||
self.output.clear()
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
|
||||
|
||||
@ -209,7 +214,11 @@ class BasePredictor:
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
|
||||
|
||||
self.run_callbacks("on_predict_end")
|
||||
return self.all_outputs
|
||||
|
||||
def predict_cli(self, source=None, model=None, return_outputs=False):
|
||||
# as __call__ is a genertor now so have to treat it like a genertor
|
||||
for _ in (self.__call__(source, model, return_outputs)):
|
||||
pass
|
||||
|
||||
def show(self, p):
|
||||
im0 = self.annotator.result()
|
||||
|
||||
@ -70,7 +70,7 @@ def select_device(device='', batch_size=0, newline=False):
|
||||
elif device: # non-cpu device requested
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
|
||||
assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
|
||||
f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
|
||||
f"Invalid CUDA 'device={device}' requested, use 'device=cpu' or pass valid CUDA device(s)"
|
||||
|
||||
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
|
||||
devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
|
||||
|
||||
@ -39,7 +39,8 @@ class ClassificationPredictor(BasePredictor):
|
||||
self.annotator = self.get_annotator(im0)
|
||||
|
||||
prob = preds[idx].softmax(0)
|
||||
self.all_outputs.append(prob)
|
||||
if self.return_outputs:
|
||||
self.output["prob"] = prob.cpu().numpy()
|
||||
# Print results
|
||||
top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
|
||||
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
|
||||
@ -62,7 +63,7 @@ def predict(cfg):
|
||||
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
|
||||
|
||||
predictor = ClassificationPredictor(cfg)
|
||||
predictor()
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -143,6 +143,7 @@ def train(cfg):
|
||||
cfg.weight_decay = 5e-5
|
||||
cfg.label_smoothing = 0.1
|
||||
cfg.warmup_epochs = 0.0
|
||||
cfg.device = cfg.device if cfg.device is not None else ''
|
||||
# trainer = ClassificationTrainer(cfg)
|
||||
# trainer.train()
|
||||
from ultralytics import YOLO
|
||||
|
||||
@ -53,12 +53,15 @@ class DetectionPredictor(BasePredictor):
|
||||
self.annotator = self.get_annotator(im0)
|
||||
|
||||
det = preds[idx]
|
||||
self.all_outputs.append(det)
|
||||
if len(det) == 0:
|
||||
return log_string
|
||||
for c in det[:, 5].unique():
|
||||
n = (det[:, 5] == c).sum() # detections per class
|
||||
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
|
||||
|
||||
if self.return_outputs:
|
||||
self.output["det"] = det.cpu().numpy()
|
||||
|
||||
# write
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
@ -89,7 +92,7 @@ def predict(cfg):
|
||||
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
|
||||
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
|
||||
predictor = DetectionPredictor(cfg)
|
||||
predictor()
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -199,6 +199,7 @@ class Loss:
|
||||
def train(cfg):
|
||||
cfg.model = cfg.model or "yolov8n.yaml"
|
||||
cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
|
||||
cfg.device = cfg.device if cfg.device is not None else ''
|
||||
# trainer = DetectionTrainer(cfg)
|
||||
# trainer.train()
|
||||
from ultralytics import YOLO
|
||||
|
||||
@ -58,10 +58,10 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
return log_string
|
||||
# Segments
|
||||
mask = masks[idx]
|
||||
if self.args.save_txt:
|
||||
if self.args.save_txt or self.return_outputs:
|
||||
shape = im0.shape if self.args.retina_masks else im.shape[2:]
|
||||
segments = [
|
||||
ops.scale_segments(im0.shape if self.args.retina_masks else im.shape[2:], x, im0.shape, normalize=True)
|
||||
for x in reversed(ops.masks2segments(mask))]
|
||||
ops.scale_segments(shape, x, im0.shape, normalize=False) for x in reversed(ops.masks2segments(mask))]
|
||||
|
||||
# Print results
|
||||
for c in det[:, 5].unique():
|
||||
@ -76,12 +76,17 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
255 if self.args.retina_masks else im[idx])
|
||||
|
||||
det = reversed(det[:, :6])
|
||||
self.all_outputs.append([det, mask])
|
||||
if self.return_outputs:
|
||||
self.output["det"] = det.cpu().numpy()
|
||||
self.output["segment"] = segments
|
||||
|
||||
# Write results
|
||||
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
|
||||
for j, (*xyxy, conf, cls) in enumerate(det):
|
||||
if self.args.save_txt: # Write to file
|
||||
seg = segments[j].reshape(-1) # (n,2) to (n*2)
|
||||
seg = segments[j].copy()
|
||||
seg[:, 0] /= shape[1] # width
|
||||
seg[:, 1] /= shape[0] # height
|
||||
seg = seg.reshape(-1) # (n,2) to (n*2)
|
||||
line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # label format
|
||||
with open(f'{self.txt_path}.txt', 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
@ -106,7 +111,7 @@ def predict(cfg):
|
||||
cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
|
||||
|
||||
predictor = SegmentationPredictor(cfg)
|
||||
predictor()
|
||||
predictor.predict_cli()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -144,6 +144,7 @@ class SegLoss(Loss):
|
||||
def train(cfg):
|
||||
cfg.model = cfg.model or "yolov8n-seg.yaml"
|
||||
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
|
||||
cfg.device = cfg.device if cfg.device is not None else ''
|
||||
# trainer = SegmentationTrainer(cfg)
|
||||
# trainer.train()
|
||||
from ultralytics import YOLO
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user