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Release 8.0.4 fixes (ultralytics#256)
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <[email protected]>
Co-authored-by: Laughing <[email protected]>
Co-authored-by: TechieG <[email protected]>
Co-authored-by: Parthiban Marimuthu <[email protected]>
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@AyushExel @pre-commit-ci
@glenn-jocher @Laughing-q @gokulnath30 @paartheee
6 people authored Jan 11, 2023
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48 changes: 24 additions & 24 deletions README.md
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Expand Up @@ -99,8 +99,8 @@ results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = YOLO("yolov8n.pt").export(format="onnx") # export a model to ONNX format
```

[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/ultralytics/releases).
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases).

### Known Issues / TODOs

Expand All @@ -116,18 +116,18 @@ We are still working on several parts of YOLOv8! We aim to have these completed

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.

[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on first use.

<details open><summary>Detection</summary>

| 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) |
| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 |
| 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) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
Expand All @@ -138,13 +138,13 @@ Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on fi

<details><summary>Segmentation</summary>

| 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) |
| --------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 |
| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 |
| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 |
| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 |
| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 |
| 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) |
| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
Expand All @@ -155,13 +155,13 @@ Ultralytics [release](https://github.com/ultralytics/ultralytics/releases) on fi

<details><summary>Classification</summary>

| 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 |
| --------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ |
| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 |
| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 |
| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 |
| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 |
| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 |
| 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 |
| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset.
<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
Expand Down
4 changes: 2 additions & 2 deletions README.zh-CN.md
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Expand Up @@ -95,7 +95,7 @@ results = model("https://ultralytics.com/images/bus.jpg") # 预测图像
success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX 格式
```

[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。
[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。

### 已知问题 / 待办事项

Expand All @@ -111,7 +111,7 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX

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。

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。
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。

<details open><summary>目标检测</summary>

Expand Down
6 changes: 3 additions & 3 deletions docs/cli.md
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Expand Up @@ -55,16 +55,16 @@ You can override config file entirely by passing a new file. You can create a co
```bash
yolo task=init
```
You can then use special `--cfg name.yaml` command to pass the new config file
You can then use `cfg=name.yaml` command to pass the new config file
```bash
yolo task=detect mode=train {++ --cfg default.yaml ++}
yolo cfg=default.yaml
```

??? example
=== "Command"
```
yolo task=init
yolo task=detect mode=train --cfg default.yaml
yolo cfg=default.yaml
```
=== "Result"
TODO: add terminal output
Expand Down
2 changes: 1 addition & 1 deletion tests/test_cli.py
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Expand Up @@ -23,7 +23,7 @@ def test_train_seg():


def test_train_cls():
os.system(f'yolo mode=train task=classify model={CFG}-cls.yaml data=imagenette160 imgsz=32 epochs=1')
os.system(f'yolo mode=train task=classify model={CFG}-cls.yaml data=mnist160 imgsz=32 epochs=1')


# Val checks -----------------------------------------------------------------------------------------------------------
Expand Down
28 changes: 14 additions & 14 deletions tests/test_engine.py
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Expand Up @@ -26,8 +26,10 @@ def test_detect():

# predictor
pred = detect.DetectionPredictor(overrides={"imgsz": [640, 640]})
p = pred(source=SOURCE, model="yolov8n.pt")
assert len(p) == 2, "predictor test failed"
i = 0
for _ in pred(source=SOURCE, model="yolov8n.pt"):
i += 1
assert i == 2, "predictor test failed"

overrides["resume"] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
Expand Down Expand Up @@ -57,8 +59,10 @@ def test_segment():

# predictor
pred = segment.SegmentationPredictor(overrides={"imgsz": [640, 640]})
p = pred(source=SOURCE, model="yolov8n-seg.pt")
assert len(p) == 2, "predictor test failed"
i = 0
for _ in pred(source=SOURCE, model="yolov8n-seg.pt"):
i += 1
assert i == 2, "predictor test failed"

# test resume
overrides["resume"] = trainer.last
Expand All @@ -73,14 +77,8 @@ def test_segment():


def test_classify():
overrides = {
"data": "imagenette160",
"model": "yolov8n-cls.yaml",
"imgsz": 32,
"epochs": 1,
"batch": 64,
"save": False}
CFG.data = "imagenette160"
overrides = {"data": "mnist160", "model": "yolov8n-cls.yaml", "imgsz": 32, "epochs": 1, "batch": 64, "save": False}
CFG.data = "mnist160"
CFG.imgsz = 32
CFG.batch = 64
# YOLO(CFG_SEG).train(**overrides) # This works
Expand All @@ -95,5 +93,7 @@ def test_classify():

# predictor
pred = classify.ClassificationPredictor(overrides={"imgsz": [640, 640]})
p = pred(source=SOURCE, model=trained_model)
assert len(p) == 2, "Predictor test failed!"
i = 0
for _ in pred(source=SOURCE, model=trained_model):
i += 1
assert i == 2, "predictor test failed"
2 changes: 1 addition & 1 deletion tests/test_python.py
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Expand Up @@ -32,7 +32,7 @@ def test_model_fuse():

def test_predict_dir():
model = YOLO(MODEL)
model.predict(source=ROOT / "assets")
model.predict(source=ROOT / "assets", return_outputs=False)


def test_val():
Expand Down
1 change: 1 addition & 0 deletions ultralytics/nn/autobackend.py
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Expand Up @@ -56,6 +56,7 @@ def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False,
fp16 &= pt or jit or onnx or engine or nn_module # FP16
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
stride = 32 # default stride
model = None # TODO: resolves ONNX inference, verify effect on other backends
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
if not (pt or triton or nn_module):
w = attempt_download(w) # download if not local
Expand Down
6 changes: 5 additions & 1 deletion ultralytics/yolo/cli.py
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Expand Up @@ -6,6 +6,7 @@
import hydra

from ultralytics import hub, yolo
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr

DIR = Path(__file__).parent
Expand All @@ -20,6 +21,9 @@ def cli(cfg):
cfg (DictConfig): Configuration for the task and mode.
"""
# LOGGER.info(f"{colorstr(f'Ultralytics YOLO v{ultralytics.__version__}')}")
if cfg.cfg:
LOGGER.info(f"Overriding default config with {cfg.cfg}")
cfg = get_config(cfg.cfg)
task, mode = cfg.task.lower(), cfg.mode.lower()

# Special case for initializing the configuration
Expand All @@ -28,7 +32,7 @@ def cli(cfg):
LOGGER.info(f"""
{colorstr("YOLO:")} configuration saved to {Path.cwd() / DEFAULT_CONFIG.name}.
To run experiments using custom configuration:
yolo task='task' mode='mode' --config-name config_file.yaml
yolo cfg=config_file.yaml
""")
return

Expand Down
1 change: 1 addition & 0 deletions ultralytics/yolo/configs/default.yaml
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Expand Up @@ -101,6 +101,7 @@ mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)

# Hydra configs --------------------------------------------------------------------------------------------------------
cfg: null # for overriding defaults.yaml
hydra:
output_subdir: null # disable hydra directory creation
run:
Expand Down
10 changes: 6 additions & 4 deletions ultralytics/yolo/engine/model.py
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Expand Up @@ -111,7 +111,7 @@ def fuse(self):
self.model.fuse()

@smart_inference_mode()
def predict(self, source, **kwargs):
def predict(self, source, return_outputs=True, **kwargs):
"""
Visualize prediction.
Expand All @@ -127,8 +127,8 @@ def predict(self, source, **kwargs):
predictor = self.PredictorClass(overrides=overrides)

predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size
predictor.setup(model=self.model, source=source)
return predictor()
predictor.setup(model=self.model, source=source, return_outputs=return_outputs)
return predictor() if return_outputs else predictor.predict_cli()

@smart_inference_mode()
def val(self, data=None, **kwargs):
Expand Down Expand Up @@ -212,10 +212,12 @@ def _guess_ops_from_task(self, task):

@staticmethod
def _reset_ckpt_args(args):
args.pop("device", None)
args.pop("project", None)
args.pop("name", None)
args.pop("batch", None)
args.pop("epochs", None)
args.pop("cache", None)
args.pop("save_json", None)

# set device to '' to prevent from auto DDP usage
args["device"] = ''
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