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4
README.md
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@ -98,8 +98,8 @@ model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
results = model.train(data="coco128.yaml", epochs=3) # train the model
results = model.val() # evaluate model performance on the validation set
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX format
```

148
docs/cfg.md
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@ -66,49 +66,50 @@ include the choice of optimizer, the choice of loss function, and the size and c
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
task.
| Key | Value | Description |
|-----------------|--------|-----------------------------------------------------------------------------|
| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| data | null | path to data file, i.e. i.e. coco128.yaml |
| epochs | 100 | number of epochs to train for |
| patience | 50 | epochs to wait for no observable improvement for early stopping of training |
| batch | 16 | number of images per batch (-1 for AutoBatch) |
| imgsz | 640 | size of input images as integer or w,h |
| save | True | save train checkpoints and predict results |
| cache | False | True/ram, disk or False. Use cache for data loading |
| device | null | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
| workers | 8 | number of worker threads for data loading (per RANK if DDP) |
| project | null | project name |
| name | null | experiment name |
| exist_ok | False | whether to overwrite existing experiment |
| pretrained | False | whether to use a pretrained model |
| optimizer | 'SGD' | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] |
| verbose | False | whether to print verbose output |
| seed | 0 | random seed for reproducibility |
| deterministic | True | whether to enable deterministic mode |
| single_cls | False | train multi-class data as single-class |
| image_weights | False | use weighted image selection for training |
| rect | False | support rectangular training |
| cos_lr | False | use cosine learning rate scheduler |
| close_mosaic | 10 | disable mosaic augmentation for final 10 epochs |
| resume | False | resume training from last checkpoint |
| lr0 | 0.01 | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| lrf | 0.01 | final learning rate (lr0 * lrf) |
| momentum | 0.937 | SGD momentum/Adam beta1 |
| weight_decay | 0.0005 | optimizer weight decay 5e-4 |
| warmup_epochs | 3.0 | warmup epochs (fractions ok) |
| warmup_momentum | 0.8 | warmup initial momentum |
| warmup_bias_lr | 0.1 | warmup initial bias lr |
| box | 7.5 | box loss gain |
| cls | 0.5 | cls loss gain (scale with pixels) |
| dfl | 1.5 | dfl loss gain |
| fl_gamma | 0.0 | focal loss gamma (efficientDet default gamma=1.5) |
| label_smoothing | 0.0 | label smoothing (fraction) |
| nbs | 64 | nominal batch size |
| overlap_mask | True | masks should overlap during training (segment train only) |
| mask_ratio | 4 | mask downsample ratio (segment train only) |
| dropout | 0.0 | use dropout regularization (classify train only) |
| val | True | validate/test during training |
| Key | Value | Description |
|-----------------|--------|--------------------------------------------------------------------------------|
| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| data | null | path to data file, i.e. i.e. coco128.yaml |
| epochs | 100 | number of epochs to train for |
| patience | 50 | epochs to wait for no observable improvement for early stopping of training |
| batch | 16 | number of images per batch (-1 for AutoBatch) |
| imgsz | 640 | size of input images as integer or w,h |
| save | True | save train checkpoints and predict results |
| cache | False | True/ram, disk or False. Use cache for data loading |
| device | null | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
| workers | 8 | number of worker threads for data loading (per RANK if DDP) |
| project | null | project name |
| name | null | experiment name |
| exist_ok | False | whether to overwrite existing experiment |
| pretrained | False | whether to use a pretrained model |
| optimizer | 'SGD' | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] |
| verbose | False | whether to print verbose output |
| seed | 0 | random seed for reproducibility |
| deterministic | True | whether to enable deterministic mode |
| single_cls | False | train multi-class data as single-class |
| image_weights | False | use weighted image selection for training |
| rect | False | support rectangular training |
| cos_lr | False | use cosine learning rate scheduler |
| close_mosaic | 10 | disable mosaic augmentation for final 10 epochs |
| resume | False | resume training from last checkpoint |
| lr0 | 0.01 | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| lrf | 0.01 | final learning rate (lr0 * lrf) |
| momentum | 0.937 | SGD momentum/Adam beta1 |
| weight_decay | 0.0005 | optimizer weight decay 5e-4 |
| warmup_epochs | 3.0 | warmup epochs (fractions ok) |
| warmup_momentum | 0.8 | warmup initial momentum |
| warmup_bias_lr | 0.1 | warmup initial bias lr |
| box | 7.5 | box loss gain |
| cls | 0.5 | cls loss gain (scale with pixels) |
| dfl | 1.5 | dfl loss gain |
| fl_gamma | 0.0 | focal loss gamma (efficientDet default gamma=1.5) |
| label_smoothing | 0.0 | label smoothing (fraction) |
| nbs | 64 | nominal batch size |
| overlap_mask | True | masks should overlap during training (segment train only) |
| mask_ratio | 4 | mask downsample ratio (segment train only) |
| dropout | 0.0 | use dropout regularization (classify train only) |
| val | True | validate/test during training |
| min_memory | False | minimize memory footprint loss function, choices=[False, True, <roll_out_thr>] |
### Prediction
@ -120,22 +121,28 @@ presence of additional features such as masks or multiple labels per box, and th
for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a
given task.
| Key | Value | Description |
|----------------|----------------------|---------------------------------------------------------|
| source | 'ultralytics/assets' | source directory for images or videos |
| show | False | show results if possible |
| save_txt | False | save results as .txt file |
| save_conf | False | save results with confidence scores |
| save_crop | False | save cropped images with results |
| hide_labels | False | hide labels |
| hide_conf | False | hide confidence scores |
| vid_stride | False | video frame-rate stride |
| line_thickness | 3 | bounding box thickness (pixels) |
| visualize | False | visualize model features |
| augment | False | apply image augmentation to prediction sources |
| agnostic_nms | False | class-agnostic NMS |
| retina_masks | False | use high-resolution segmentation masks |
| classes | null | filter results by class, i.e. class=0, or class=[0,2,3] |
| Key | Value | Description |
|----------------|----------------------|----------------------------------------------------------|
| source | 'ultralytics/assets' | source directory for images or videos |
| conf | 0.25 | object confidence threshold for detection |
| iou | 0.7 | intersection over union (IoU) threshold for NMS |
| half | False | use half precision (FP16) |
| device | null | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| show | False | show results if possible |
| save_txt | False | save results as .txt file |
| save_conf | False | save results with confidence scores |
| save_crop | False | save cropped images with results |
| hide_labels | False | hide labels |
| hide_conf | False | hide confidence scores |
| max_det | 300 | maximum number of detections per image |
| vid_stride | False | video frame-rate stride |
| line_thickness | 3 | bounding box thickness (pixels) |
| visualize | False | visualize model features |
| augment | False | apply image augmentation to prediction sources |
| agnostic_nms | False | class-agnostic NMS |
| retina_masks | False | use high-resolution segmentation masks |
| classes | null | filter results by class, i.e. class=0, or class=[0,2,3] |
| box | True | Show boxes in segmentation predictions |
### Validation
@ -147,17 +154,18 @@ process include the size and composition of the validation dataset and the speci
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
validation dataset and to detect and prevent overfitting.
| Key | Value | Description |
|-------------|-------|-----------------------------------------------------------------------------|
| save_json | False | save results to JSON file |
| save_hybrid | False | save hybrid version of labels (labels + additional predictions) |
| conf | 0.001 | object confidence threshold for detection (default 0.25 predict, 0.001 val) |
| iou | 0.6 | intersection over union (IoU) threshold for NMS |
| max_det | 300 | maximum number of detections per image |
| half | True | use half precision (FP16) |
| dnn | False | use OpenCV DNN for ONNX inference |
| plots | False | show plots during training |
| rect | False | support rectangular evaluation |
| Key | Value | Description |
|-------------|-------|-----------------------------------------------------------------|
| save_json | False | save results to JSON file |
| save_hybrid | False | save hybrid version of labels (labels + additional predictions) |
| conf | 0.001 | object confidence threshold for detection |
| iou | 0.6 | intersection over union (IoU) threshold for NMS |
| max_det | 300 | maximum number of detections per image |
| half | True | use half precision (FP16) |
| device | null | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| dnn | False | use OpenCV DNN for ONNX inference |
| plots | False | show plots during training |
| rect | False | support rectangular evaluation |
### Export

94
docs/cli.md
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@ -35,25 +35,10 @@ the [Configuration](cfg.md) page.
!!! example ""
=== "CLI"
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
```
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train resume model=last.pt # resume training
```
## Val
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
@ -61,77 +46,30 @@ training `data` and arguments as model attributes.
!!! example ""
=== "CLI"
```bash
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
results = model.val() # no arguments needed, dataset and settings remembered
```
```bash
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLOv8n model to run predictions on images.
!!! example ""
=== "CLI"
```bash
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
```bash
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example ""
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained
# Export the model
model.export(format="onnx")
```
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8 export formats include:

4
docs/python.md
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@ -20,7 +20,9 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
=== "Resume"
```python
TODO: Resume feature is under development and should be released soon.
# TODO: Resume feature is under development and should be released soon.
model = YOLO("last.pt")
model.train(resume=True)
```
!!! example "Val"

7
docs/tasks/classification.md
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@ -30,7 +30,7 @@ see the [Configuration](../cfg.md) page.
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="mnist160", epochs=100, imgsz=64)
model.train(data="mnist160", epochs=100, imgsz=64)
```
=== "CLI"
@ -55,7 +55,9 @@ it's training `data` and arguments as model attributes.
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
results = model.val() # no arguments needed, dataset and settings remembered
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5 # top5 accuracy
```
=== "CLI"
@ -88,6 +90,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
yolo classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
## Export

9
docs/tasks/detection.md
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@ -30,7 +30,7 @@ the [Configuration](../cfg.md) page.
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
model.train(data="coco128.yaml", epochs=100, imgsz=640)
```
=== "CLI"
@ -55,7 +55,11 @@ training `data` and arguments as model attributes.
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
results = model.val() # no arguments needed, dataset and settings remembered
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95
metrics.box.map50 # map50
metrics.box.map75 # map75
metrics.box.maps # a list contains map50-95 of each category
```
=== "CLI"
@ -88,6 +92,7 @@ Use a trained YOLOv8n model to run predictions on images.
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
## Export

13
docs/tasks/segmentation.md
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@ -30,7 +30,7 @@ arguments see the [Configuration](../cfg.md) page.
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
```
=== "CLI"
@ -55,7 +55,15 @@ retains it's training `data` and arguments as model attributes.
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
results = model.val() # no arguments needed, dataset and settings remembered
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.box.map50 # map50(B)
metrics.box.map75 # map75(B)
metrics.box.maps # a list contains map50-95(B) of each category
metrics.seg.map # map50-95(M)
metrics.seg.map50 # map50(M)
metrics.seg.map75 # map75(M)
metrics.seg.maps # a list contains map50-95(M) of each category
```
=== "CLI"
@ -88,6 +96,7 @@ Use a trained YOLOv8n-seg model to run predictions on images.
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
## Export

1
ultralytics/yolo/engine/model.py
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@ -168,6 +168,7 @@ class YOLO:
validator = self.ValidatorClass(args=args)
validator(model=self.model)
return validator.metrics
@smart_inference_mode()
def export(self, **kwargs):

56
ultralytics/yolo/utils/metrics.py
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@ -418,6 +418,7 @@ class Metric:
self.f1 = [] # (nc, )
self.all_ap = [] # (nc, 10)
self.ap_class_index = [] # (nc, )
self.nc = 0
@property
def ap50(self):
@ -459,6 +460,14 @@ class Metric:
"""
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
@property
def map75(self):
"""Mean AP@0.75 of all classes.
Return:
float.
"""
return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0
@property
def map(self):
"""Mean AP@0.5:0.95 of all classes.
@ -475,8 +484,10 @@ class Metric:
"""class-aware result, return p[i], r[i], ap50[i], ap[i]"""
return self.p[i], self.r[i], self.ap50[i], self.ap[i]
def get_maps(self, nc):
maps = np.zeros(nc) + self.map
@property
def maps(self):
"""mAP of each class"""
maps = np.zeros(self.nc) + self.map
for i, c in enumerate(self.ap_class_index):
maps[c] = self.ap[i]
return maps
@ -500,33 +511,35 @@ class DetMetrics:
self.save_dir = save_dir
self.plot = plot
self.names = names
self.metric = Metric()
self.box = Metric()
def process(self, tp, conf, pred_cls, target_cls):
results = ap_per_class(tp, conf, pred_cls, target_cls, plot=self.plot, save_dir=self.save_dir,
names=self.names)[2:]
self.metric.update(results)
self.box.nc = len(self.names)
self.box.update(results)
@property
def keys(self):
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
def mean_results(self):
return self.metric.mean_results()
return self.box.mean_results()
def class_result(self, i):
return self.metric.class_result(i)
return self.box.class_result(i)
def get_maps(self, nc):
return self.metric.get_maps(nc)
@property
def maps(self):
return self.box.maps
@property
def fitness(self):
return self.metric.fitness()
return self.box.fitness()
@property
def ap_class_index(self):
return self.metric.ap_class_index
return self.box.ap_class_index
@property
def results_dict(self):
@ -539,8 +552,8 @@ class SegmentMetrics:
self.save_dir = save_dir
self.plot = plot
self.names = names
self.metric_box = Metric()
self.metric_mask = Metric()
self.box = Metric()
self.seg = Metric()
def process(self, tp_m, tp_b, conf, pred_cls, target_cls):
results_mask = ap_per_class(tp_m,
@ -551,7 +564,8 @@ class SegmentMetrics:
save_dir=self.save_dir,
names=self.names,
prefix="Mask")[2:]
self.metric_mask.update(results_mask)
self.seg.nc = len(self.names)
self.seg.update(results_mask)
results_box = ap_per_class(tp_b,
conf,
pred_cls,
@ -560,7 +574,8 @@ class SegmentMetrics:
save_dir=self.save_dir,
names=self.names,
prefix="Box")[2:]
self.metric_box.update(results_box)
self.box.nc = len(self.names)
self.box.update(results_box)
@property
def keys(self):
@ -569,22 +584,23 @@ class SegmentMetrics:
"metrics/precision(M)", "metrics/recall(M)", "metrics/mAP50(M)", "metrics/mAP50-95(M)"]
def mean_results(self):
return self.metric_box.mean_results() + self.metric_mask.mean_results()
return self.box.mean_results() + self.seg.mean_results()
def class_result(self, i):
return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
return self.box.class_result(i) + self.seg.class_result(i)
def get_maps(self, nc):
return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
@property
def maps(self):
return self.box.maps + self.seg.maps
@property
def fitness(self):
return self.metric_mask.fitness() + self.metric_box.fitness()
return self.seg.fitness() + self.box.fitness()
@property
def ap_class_index(self):
# boxes and masks have the same ap_class_index
return self.metric_box.ap_class_index
return self.box.ap_class_index
@property
def results_dict(self):

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