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

98
docs/cli.md
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@ -35,103 +35,41 @@ the [Configuration](cfg.md) page.
!!! example "" !!! example ""
=== "CLI" ```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
```bash yolo detect train resume model=last.pt # resume training
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)
```
## Val ## Val
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
training `data` and arguments as model attributes. training `data` and arguments as model attributes.
!!! example "" !!! example ""
=== "CLI" ```bash
yolo detect val model=yolov8n.pt # val official model
```bash yolo detect val model=path/to/best.pt # val custom model
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
```
## Predict ## Predict
Use a trained YOLOv8n model to run predictions on images. Use a trained YOLOv8n model to run predictions on images.
!!! example "" !!! example ""
=== "CLI" ```bash
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
```bash yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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
```
## Export ## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc. Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example "" !!! 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 ```bash
from ultralytics import YOLO yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
# 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")
```
Available YOLOv8 export formats include: Available YOLOv8 export formats include:
@ -194,4 +132,4 @@ like `imgsz=320` in this example:
```bash ```bash
yolo copy-cfg yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320 yolo cfg=default_copy.yaml imgsz=320
``` ```

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docs/python.md
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@ -20,7 +20,9 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
=== "Resume" === "Resume"
```python ```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" !!! example "Val"

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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) model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
# Train the model # Train the model
results = model.train(data="mnist160", epochs=100, imgsz=64) model.train(data="mnist160", epochs=100, imgsz=64)
``` ```
=== "CLI" === "CLI"
@ -55,7 +55,9 @@ it's training `data` and arguments as model attributes.
model = YOLO("path/to/best.pt") # load a custom model model = YOLO("path/to/best.pt") # load a custom model
# Validate the 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" === "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=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 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 ## Export

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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) model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model # Train the model
results = model.train(data="coco128.yaml", epochs=100, imgsz=640) model.train(data="coco128.yaml", epochs=100, imgsz=640)
``` ```
=== "CLI" === "CLI"
@ -55,7 +55,11 @@ training `data` and arguments as model attributes.
model = YOLO("path/to/best.pt") # load a custom model model = YOLO("path/to/best.pt") # load a custom model
# Validate the 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" === "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=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 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 ## Export

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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) model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
# Train the model # 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" === "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 model = YOLO("path/to/best.pt") # load a custom model
# Validate the 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" === "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=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 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 ## Export

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

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

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