Rename `img_size` to `imgsz` (#86)

.github/workflows/ci.yaml

@@ -90,16 +90,16 @@ jobs:
       - name: Test detection
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=1 img_size=64
+          yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=1 imgsz=64
-          yolo task=detect mode=val model=runs/exp/weights/last.pt img_size=64
+          yolo task=detect mode=val model=runs/exp/weights/last.pt imgsz=64
       - name: Test segmentation
         shell: bash  # for Windows compatibility
         # TODO: redo val test without hardcoded weights
         run: |
-          yolo task=segment mode=train model=yolov5n-seg.yaml data=coco128-seg.yaml epochs=1 img_size=64
+          yolo task=segment mode=train model=yolov5n-seg.yaml data=coco128-seg.yaml epochs=1 imgsz=64
-          yolo task=segment mode=val model=runs/exp2/weights/last.pt data=coco128-seg.yaml img_size=64
+          yolo task=segment mode=val model=runs/exp2/weights/last.pt data=coco128-seg.yaml imgsz=64
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=classify mode=train model=resnet18 data=mnist160 epochs=1 img_size=32
+          yolo task=classify mode=train model=resnet18 data=mnist160 epochs=1 imgsz=32
           yolo task=classify mode=val model=runs/exp3/weights/last.pt data=mnist160

docs/conf.md

@@ -21,7 +21,7 @@ Default training settings and hyperparameters for medium-augmentation COCO train
     | epochs          | 100    | Number of epochs to train                                                       |
     | workers         | 8      | Number of cpu workers used per process. Scales automatically with DDP           |
     | batch_size      | 16     | Batch size of the dataloader                                                    |
-    | img_size        | 640    | Image size of data in dataloader                                                |
+    | imgsz        | 640    | Image size of data in dataloader                                                |
     | optimizer       | SGD    | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp`               |
     | single_cls      | False  | Train on multi-class data as single-class                                       |
     | image_weights   | False  | Use weighted image selection for training                                       |

ultralytics/tests/data/dataloader/yolopose.py

@@ -70,7 +70,7 @@ with open("ultralytics/tests/data/dataloader/hyp_test.yaml") as f:
 def test(augment, rect):
     dataloader, _ = build_dataloader(
         img_path="/d/dataset/COCO/images/val2017",
-        img_size=640,
+        imgsz=640,
         label_path=None,
         cache=False,
         hyp=hyp,

ultralytics/tests/test_model.py

@@ -36,13 +36,13 @@ def test_visualize_preds():
 def test_val():
     model = YOLO()
     model.load("balloon-segment.pt")
-    model.val(data="coco128-seg.yaml", img_size=32)
+    model.val(data="coco128-seg.yaml", imgsz=32)
 
 
 def test_model_resume():
     model = YOLO()
     model.new("yolov5n-seg.yaml")
-    model.train(epochs=1, img_size=32, data="coco128-seg.yaml")
+    model.train(epochs=1, imgsz=32, data="coco128-seg.yaml")
     try:
         model.resume(task="segment")
     except AssertionError:
@@ -52,9 +52,9 @@ def test_model_resume():
 def test_model_train_pretrained():
     model = YOLO()
     model.load("balloon-detect.pt")
-    model.train(data="coco128.yaml", epochs=1, img_size=32)
+    model.train(data="coco128.yaml", epochs=1, imgsz=32)
     model.new("yolov5n.yaml")
-    model.train(data="coco128.yaml", epochs=1, img_size=32)
+    model.train(data="coco128.yaml", epochs=1, imgsz=32)
     img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512)
     model(img)
 

ultralytics/yolo/data/augment.py

@@ -114,15 +114,15 @@ class BaseMixTransform:
 class Mosaic(BaseMixTransform):
     """Mosaic augmentation.
     Args:
-        img_size (Sequence[int]): Image size after mosaic pipeline of single
+        imgsz (Sequence[int]): Image size after mosaic pipeline of single
             image. The shape order should be (height, width).
             Default to (640, 640).
     """
 
-    def __init__(self, img_size=640, p=1.0, border=(0, 0)):
+    def __init__(self, imgsz=640, p=1.0, border=(0, 0)):
         assert 0 <= p <= 1.0, "The probability should be in range [0, 1]. " f"got {p}."
         super().__init__(pre_transform=None, p=p)
-        self.img_size = img_size
+        self.imgsz = imgsz
         self.border = border
 
     def get_indexes(self, dataset):
@@ -132,7 +132,7 @@ class Mosaic(BaseMixTransform):
         mosaic_labels = []
         assert labels.get("rect_shape", None) is None, "rect and mosaic is exclusive."
         assert len(labels.get("mix_labels", [])) > 0, "There are no other images for mosaic augment."
-        s = self.img_size
+        s = self.imgsz
         yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border)  # mosaic center x, y
         mix_labels = labels["mix_labels"]
         for i in range(4):
@@ -184,12 +184,12 @@ class Mosaic(BaseMixTransform):
             instances.append(labels["instances"])
         final_labels = {
             "ori_shape": mosaic_labels[0]["ori_shape"],
-            "resized_shape": (self.img_size * 2, self.img_size * 2),
+            "resized_shape": (self.imgsz * 2, self.imgsz * 2),
             "im_file": mosaic_labels[0]["im_file"],
             "cls": np.concatenate(cls, 0)}
 
         final_labels["instances"] = Instances.concatenate(instances, axis=0)
-        final_labels["instances"].clip(self.img_size * 2, self.img_size * 2)
+        final_labels["instances"].clip(self.imgsz * 2, self.imgsz * 2)
         return final_labels
 
 
@@ -658,9 +658,9 @@ class Format:
         return masks, instances, cls
 
 
-def mosaic_transforms(img_size, hyp):
+def mosaic_transforms(imgsz, hyp):
     pre_transform = Compose([
-        Mosaic(img_size=img_size, p=hyp.mosaic, border=[-img_size // 2, -img_size // 2]),
+        Mosaic(imgsz=imgsz, p=hyp.mosaic, border=[-imgsz // 2, -imgsz // 2]),
         CopyPaste(p=hyp.copy_paste),
         RandomPerspective(
             degrees=hyp.degrees,
@@ -668,7 +668,7 @@ def mosaic_transforms(img_size, hyp):
             scale=hyp.scale,
             shear=hyp.shear,
             perspective=hyp.perspective,
-            border=[-img_size // 2, -img_size // 2],
+            border=[-imgsz // 2, -imgsz // 2],
         ),])
     return Compose([
         pre_transform,
@@ -682,9 +682,9 @@ def mosaic_transforms(img_size, hyp):
         RandomFlip(direction="horizontal", p=hyp.fliplr),])  # transforms
 
 
-def affine_transforms(img_size, hyp):
+def affine_transforms(imgsz, hyp):
     return Compose([
-        LetterBox(new_shape=(img_size, img_size)),
+        LetterBox(new_shape=(imgsz, imgsz)),
         RandomPerspective(
             degrees=hyp.degrees,
             translate=hyp.translate,

ultralytics/yolo/data/base.py

@@ -24,7 +24,7 @@ class BaseDataset(Dataset):
     def __init__(
         self,
         img_path,
-        img_size=640,
+        imgsz=640,
         label_path=None,
         cache=False,
         augment=True,
@@ -38,7 +38,7 @@ class BaseDataset(Dataset):
     ):
         super().__init__()
         self.img_path = img_path
-        self.img_size = img_size
+        self.imgsz = imgsz
         self.label_path = label_path
         self.augment = augment
         self.prefix = prefix
@@ -118,7 +118,7 @@ class BaseDataset(Dataset):
                 im = cv2.imread(f)  # BGR
                 assert im is not None, f"Image Not Found {f}"
             h0, w0 = im.shape[:2]  # orig hw
-            r = self.img_size / max(h0, w0)  # ratio
+            r = self.imgsz / max(h0, w0)  # ratio
             if r != 1:  # if sizes are not equal
                 interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
                 im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
@@ -168,7 +168,7 @@ class BaseDataset(Dataset):
             elif mini > 1:
                 shapes[i] = [1, 1 / mini]
 
-        self.batch_shapes = np.ceil(np.array(shapes) * self.img_size / self.stride + self.pad).astype(int) * self.stride
+        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
         self.batch = bi  # batch index of image
 
     def __getitem__(self, index):

ultralytics/yolo/data/build.py

@@ -62,7 +62,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
         dataset = YOLODataset(
             img_path=img_path,
             label_path=label_path,
-            img_size=cfg.img_size,
+            imgsz=cfg.imgsz,
             batch_size=batch_size,
             augment=True if mode == "train" else False,  # augmentation
             hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -18,10 +18,10 @@ from ultralytics.yolo.utils.checks import check_requirements
 
 class LoadStreams:
     # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
-    def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+    def __init__(self, sources='file.streams', imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
         torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
         self.mode = 'stream'
-        self.img_size = img_size
+        self.imgsz = imgsz
         self.stride = stride
         self.vid_stride = vid_stride  # video frame-rate stride
         sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
@@ -55,7 +55,7 @@ class LoadStreams:
         LOGGER.info('')  # newline
 
         # check for common shapes
-        s = np.stack([LetterBox(img_size, auto, stride=stride)(image=x).shape for x in self.imgs])
+        s = np.stack([LetterBox(imgsz, auto, stride=stride)(image=x).shape for x in self.imgs])
         self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
         self.auto = auto and self.rect
         self.transforms = transforms  # optional
@@ -92,7 +92,7 @@ class LoadStreams:
         if self.transforms:
             im = np.stack([self.transforms(x) for x in im0])  # transforms
         else:
-            im = np.stack([LetterBox(self.img_size, self.auto, stride=self.stride)(image=x) for x in im0])
+            im = np.stack([LetterBox(self.imgsz, self.auto, stride=self.stride)(image=x) for x in im0])
             im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
             im = np.ascontiguousarray(im)  # contiguous
 
@@ -104,7 +104,7 @@ class LoadStreams:
 
 class LoadScreenshots:
     # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
-    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
+    def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None):
         # source = [screen_number left top width height] (pixels)
         check_requirements('mss')
         import mss
@@ -117,7 +117,7 @@ class LoadScreenshots:
             left, top, width, height = (int(x) for x in params)
         elif len(params) == 5:
             self.screen, left, top, width, height = (int(x) for x in params)
-        self.img_size = img_size
+        self.imgsz = imgsz
         self.stride = stride
         self.transforms = transforms
         self.auto = auto
@@ -144,7 +144,7 @@ class LoadScreenshots:
         if self.transforms:
             im = self.transforms(im0)  # transforms
         else:
-            im = LetterBox(self.img_size, self.auto, stride=self.stride)(image=im0)
+            im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
             im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
             im = np.ascontiguousarray(im)  # contiguous
         self.frame += 1
@@ -153,7 +153,7 @@ class LoadScreenshots:
 
 class LoadImages:
     # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
-    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+    def __init__(self, path, imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
         if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
             path = Path(path).read_text().rsplit()
         files = []
@@ -172,7 +172,7 @@ class LoadImages:
         videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
         ni, nv = len(images), len(videos)
 
-        self.img_size = img_size
+        self.imgsz = imgsz
         self.stride = stride
         self.files = images + videos
         self.nf = ni + nv  # number of files
@@ -226,7 +226,7 @@ class LoadImages:
         if self.transforms:
             im = self.transforms(im0)  # transforms
         else:
-            im = LetterBox(self.img_size, self.auto, stride=self.stride)(image=im0)
+            im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
             im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
             im = np.ascontiguousarray(im)  # contiguous
 

ultralytics/yolo/data/dataset.py

@@ -24,7 +24,7 @@ class YOLODataset(BaseDataset):
     def __init__(
         self,
         img_path,
-        img_size=640,
+        imgsz=640,
         label_path=None,
         cache=False,
         augment=True,
@@ -41,7 +41,7 @@ class YOLODataset(BaseDataset):
         self.use_segments = use_segments
         self.use_keypoints = use_keypoints
         assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
-        super().__init__(img_path, img_size, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad,
+        super().__init__(img_path, imgsz, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad,
                          single_cls)
 
     def cache_labels(self, path=Path("./labels.cache")):
@@ -128,11 +128,11 @@ class YOLODataset(BaseDataset):
         # mosaic = False
         if self.augment:
             if mosaic:
-                transforms = mosaic_transforms(self.img_size, hyp)
+                transforms = mosaic_transforms(self.imgsz, hyp)
             else:
-                transforms = affine_transforms(self.img_size, hyp)
+                transforms = affine_transforms(self.imgsz, hyp)
         else:
-            transforms = Compose([LetterBox(new_shape=(self.img_size, self.img_size))])
+            transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz))])
         transforms.append(
             Format(bbox_format="xywh",
                    normalize=True,

ultralytics/yolo/data/dataset_wrappers.py

@@ -14,7 +14,7 @@ class MixAndRectDataset:
 
     def __init__(self, dataset):
         self.dataset = dataset
-        self.img_size = dataset.img_size
+        self.imgsz = dataset.imgsz
 
     def __len__(self):
         return len(self.dataset)

ultralytics/yolo/data/utils.py

@@ -128,50 +128,50 @@ def verify_image_label(args):
         return [None, None, None, None, None, nm, nf, ne, nc, msg]
 
 
-def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
+def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
     """
     Args:
-        img_size (tuple): The image size.
+        imgsz (tuple): The image size.
         polygons (np.ndarray): [N, M], N is the number of polygons,
             M is the number of points(Be divided by 2).
     """
-    mask = np.zeros(img_size, dtype=np.uint8)
+    mask = np.zeros(imgsz, dtype=np.uint8)
     polygons = np.asarray(polygons)
     polygons = polygons.astype(np.int32)
     shape = polygons.shape
     polygons = polygons.reshape(shape[0], -1, 2)
     cv2.fillPoly(mask, polygons, color=color)
-    nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)
+    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
     # NOTE: fillPoly firstly then resize is trying the keep the same way
     # of loss calculation when mask-ratio=1.
     mask = cv2.resize(mask, (nw, nh))
     return mask
 
 
-def polygons2masks(img_size, polygons, color, downsample_ratio=1):
+def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
     """
     Args:
-        img_size (tuple): The image size.
+        imgsz (tuple): The image size.
         polygons (list[np.ndarray]): each polygon is [N, M],
             N is the number of polygons,
             M is the number of points(Be divided by 2).
     """
     masks = []
     for si in range(len(polygons)):
-        mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)
+        mask = polygon2mask(imgsz, [polygons[si].reshape(-1)], color, downsample_ratio)
         masks.append(mask)
     return np.array(masks)
 
 
-def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
+def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
     """Return a (640, 640) overlap mask."""
-    masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
+    masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
                      dtype=np.int32 if len(segments) > 255 else np.uint8)
     areas = []
     ms = []
     for si in range(len(segments)):
         mask = polygon2mask(
-            img_size,
+            imgsz,
             [segments[si].reshape(-1)],
             downsample_ratio=downsample_ratio,
             color=1,

ultralytics/yolo/engine/model.py

@@ -111,11 +111,11 @@ class YOLO:
         predictor = self.PredictorClass(overrides=kwargs)
 
         # check size type
-        sz = predictor.args.img_size
+        sz = predictor.args.imgsz
         if type(sz) != int:  # recieved listConfig
-            predictor.args.img_size = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]]  # expand
+            predictor.args.imgsz = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]]  # expand
         else:
-            predictor.args.img_size = [sz, sz]
+            predictor.args.imgsz = [sz, sz]
 
         predictor.setup(model=self.model, source=source)
         predictor()

ultralytics/yolo/engine/predictor.py

@@ -39,7 +39,7 @@ from ultralytics.yolo.utils.configs import get_config
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
 from ultralytics.yolo.utils.plotting import Annotator
-from ultralytics.yolo.utils.torch_utils import check_img_size, select_device, smart_inference_mode
+from ultralytics.yolo.utils.torch_utils import check_imgsz, select_device, smart_inference_mode
 
 DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
 
@@ -99,18 +99,18 @@ class BasePredictor:
         self.args.half &= device.type != 'cpu'  # half precision only supported on CUDA
         model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)  # NOTE: not passing data
         stride, pt = model.stride, model.pt
-        imgsz = check_img_size(self.args.img_size, s=stride)  # check image size
+        imgsz = check_imgsz(self.args.imgsz, s=stride)  # check image size
 
         # Dataloader
         bs = 1  # batch_size
         if webcam:
             self.view_img = check_imshow(warn=True)
-            self.dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
+            self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
             bs = len(self.dataset)
         elif screenshot:
-            self.dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+            self.dataset = LoadScreenshots(source, imgsz=imgsz, stride=stride, auto=pt)
         else:
-            self.dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
+            self.dataset = LoadImages(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
         self.vid_path, self.vid_writer = [None] * bs, [None] * bs
         model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
 

ultralytics/yolo/engine/validator.py

@@ -12,7 +12,7 @@ from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.modeling import get_model
 from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
 from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import check_img_size, de_parallel, select_device
+from ultralytics.yolo.utils.torch_utils import check_imgsz, de_parallel, select_device
 
 
 class BaseValidator:
@@ -55,7 +55,7 @@ class BaseValidator:
             model = AutoBackend(model, device=self.device, dnn=self.args.dnn, fp16=self.args.half)
             self.model = model
             stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
-            imgsz = check_img_size(self.args.img_size, s=stride)
+            imgsz = check_imgsz(self.args.imgsz, s=stride)
             if engine:
                 self.args.batch_size = model.batch_size
             else:

ultralytics/yolo/utils/anchors.py

@@ -51,7 +51,7 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
     else:
         LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
         na = m.anchors.numel() // 2  # number of anchors
-        anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        anchors = kmean_anchors(dataset, n=na, imgsz=imgsz, thr=thr, gen=1000, verbose=False)
         new_bpr = metric(anchors)[0]
         if new_bpr > bpr:  # replace anchors
             anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
@@ -64,13 +64,13 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
         LOGGER.info(s)
 
 
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+def kmean_anchors(dataset='./data/coco128.yaml', n=9, imgsz=640, thr=4.0, gen=1000, verbose=True):
     """ Creates kmeans-evolved anchors from training dataset
 
         Arguments:
             dataset: path to data.yaml, or a loaded dataset
             n: number of anchors
-            img_size: image size used for training
+            imgsz: image size used for training
             thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
             gen: generations to evolve anchors using genetic algorithm
             verbose: print all results
@@ -101,7 +101,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
         x, best = metric(k, wh0)
         bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
         s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
-            f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
+            f'{PREFIX}n={n}, imgsz={imgsz}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
             f'past_thr={x[x > thr].mean():.3f}-mean: '
         for x in k:
             s += '%i,%i, ' % (round(x[0]), round(x[1]))
@@ -116,7 +116,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
         dataset = BaseDataset(data_dict['train'], augment=True, rect=True)
 
     # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
     wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
 
     # Filter
@@ -135,7 +135,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
         assert n == len(k)  # kmeans may return fewer points than requested if wh is insufficient or too similar
     except Exception:
         LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init')
-        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
+        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * imgsz  # random init
     wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
     k = print_results(k, verbose=False)
 

ultralytics/yolo/utils/configs/default.yaml

@@ -10,7 +10,7 @@ model: null  # i.e. yolov5s.pt, yolo.yaml
 data: null  # i.e. coco128.yaml
 epochs: 300
 batch_size: 16
-img_size: 640
+imgsz: 640
 nosave: False
 cache: False  # True/ram, disk or False
 device: ''  # cuda device, i.e. 0 or 0,1,2,3 or cpu

ultralytics/yolo/utils/modeling/tasks.py

@@ -51,8 +51,8 @@ class BaseModel(nn.Module):
         self.info()
         return self
 
-    def info(self, verbose=False, img_size=640):  # print model information
+    def info(self, verbose=False, imgsz=640):  # print model information
-        model_info(self, verbose, img_size)
+        model_info(self, verbose, imgsz)
 
     def _apply(self, fn):
         # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
@@ -117,7 +117,7 @@ class DetectionModel(BaseModel):
         return self._forward_once(x, profile, visualize)  # single-scale inference, train
 
     def _forward_augment(self, x):
-        img_size = x.shape[-2:]  # height, width
+        imgsz = x.shape[-2:]  # height, width
         s = [1, 0.83, 0.67]  # scales
         f = [None, 3, None]  # flips (2-ud, 3-lr)
         y = []  # outputs
@@ -125,25 +125,25 @@ class DetectionModel(BaseModel):
             xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
             yi = self._forward_once(xi)[0]  # forward
             # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-            yi = self._descale_pred(yi, fi, si, img_size)
+            yi = self._descale_pred(yi, fi, si, imgsz)
             y.append(yi)
         y = self._clip_augmented(y)  # clip augmented tails
         return torch.cat(y, 1), None  # augmented inference, train
 
-    def _descale_pred(self, p, flips, scale, img_size):
+    def _descale_pred(self, p, flips, scale, imgsz):
         # de-scale predictions following augmented inference (inverse operation)
         if self.inplace:
             p[..., :4] /= scale  # de-scale
             if flips == 2:
-                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
+                p[..., 1] = imgsz[0] - p[..., 1]  # de-flip ud
             elif flips == 3:
-                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
+                p[..., 0] = imgsz[1] - p[..., 0]  # de-flip lr
         else:
             x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
             if flips == 2:
-                y = img_size[0] - y  # de-flip ud
+                y = imgsz[0] - y  # de-flip ud
             elif flips == 3:
-                x = img_size[1] - x  # de-flip lr
+                x = imgsz[1] - x  # de-flip lr
             p = torch.cat((x, y, wh, p[..., 4:]), -1)
         return p
 

ultralytics/yolo/utils/torch_utils.py

@@ -124,7 +124,7 @@ def fuse_conv_and_bn(conv, bn):
 
 
 def model_info(model, verbose=False, imgsz=640):
-    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    # Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
     n_p = get_num_params(model)
     n_g = get_num_gradients(model)  # number gradients
     if verbose:
@@ -185,11 +185,11 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
     return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
 
 
-def check_img_size(imgsz, s=32, floor=0):
+def check_imgsz(imgsz, s=32, floor=0):
     # Verify image size is a multiple of stride s in each dimension
-    if isinstance(imgsz, int):  # integer i.e. img_size=640
+    if isinstance(imgsz, int):  # integer i.e. imgsz=640
         new_size = max(make_divisible(imgsz, int(s)), floor)
-    else:  # list i.e. img_size=[640, 480]
+    else:  # list i.e. imgsz=[640, 480]
         imgsz = list(imgsz)  # convert to list if tuple
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:

ultralytics/yolo/v8/classify/predict.py

@@ -55,11 +55,11 @@ class ClassificationPredictor(BasePredictor):
 @hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
 def predict(cfg):
     cfg.model = cfg.model or "squeezenet1_0"
-    sz = cfg.img_size
+    sz = cfg.imgsz
     if type(sz) != int:  # recieved listConfig
-        cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]]  # expand
+        cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
-        cfg.img_size = [sz, sz]
+        cfg.imgsz = [sz, sz]
     predictor = ClassificationPredictor(cfg)
     predictor()
 

ultralytics/yolo/v8/classify/train.py

@@ -36,7 +36,7 @@ class ClassificationTrainer(BaseTrainer):
 
     def get_dataloader(self, dataset_path, batch_size, rank=0, mode="train"):
         return build_classification_dataloader(path=dataset_path,
-                                               imgsz=self.args.img_size,
+                                               imgsz=self.args.imgsz,
                                                batch_size=batch_size,
                                                rank=rank)
 
@@ -70,7 +70,7 @@ def train(cfg):
 if __name__ == "__main__":
     """
     CLI usage:
-    python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 img_size=224
+    python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 imgsz=224
 
     TODO:
     Direct cli support, i.e, yolov8 classify_train args.epochs 10

ultralytics/yolo/v8/classify/val.py

@@ -28,7 +28,7 @@ class ClassificationValidator(BaseValidator):
         return {"top1": top1, "top5": top5, "fitness": top5}
 
     def get_dataloader(self, dataset_path, batch_size):
-        return build_classification_dataloader(path=dataset_path, imgsz=self.args.img_size, batch_size=batch_size)
+        return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size)
 
     @property
     def metric_keys(self):

ultralytics/yolo/v8/detect/predict.py

@@ -84,11 +84,11 @@ class DetectionPredictor(BasePredictor):
 @hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
 def predict(cfg):
     cfg.model = cfg.model or "n.pt"
-    sz = cfg.img_size
+    sz = cfg.imgsz
     if type(sz) != int:  # recieved listConfig
-        cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]]  # expand
+        cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
-        cfg.img_size = [sz, sz]
+        cfg.imgsz = [sz, sz]
     predictor = DetectionPredictor(cfg)
     predictor()
 

ultralytics/yolo/v8/detect/train.py

@@ -28,7 +28,7 @@ class DetectionTrainer(BaseTrainer):
         nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)
         self.args.box *= 3 / nl  # scale to layers
         self.args.cls *= self.data["nc"] / 80 * 3 / nl  # scale to classes and layers
-        self.args.obj *= (self.args.img_size / 640) ** 2 * 3 / nl  # scale to image size and layers
+        self.args.obj *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
         self.model.nc = self.data["nc"]  # attach number of classes to model
         self.model.args = self.args  # attach hyperparameters to model
         # TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
@@ -223,7 +223,7 @@ def train(cfg):
 if __name__ == "__main__":
     """
     CLI usage:
-    python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 img_size=640
+    python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 imgsz=640
 
     TODO:
     yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=100

ultralytics/yolo/v8/segment/predict.py

@@ -102,11 +102,11 @@ class SegmentationPredictor(DetectionPredictor):
 @hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
 def predict(cfg):
     cfg.model = cfg.model or "n.pt"
-    sz = cfg.img_size
+    sz = cfg.imgsz
     if type(sz) != int:  # recieved listConfig
-        cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]]  # expand
+        cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
-        cfg.img_size = [sz, sz]
+        cfg.imgsz = [sz, sz]
     predictor = SegmentationPredictor(cfg)
     predictor()
 

ultralytics/yolo/v8/segment/train.py

@@ -243,7 +243,7 @@ def train(cfg):
 if __name__ == "__main__":
     """
     CLI usage:
-    python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 img_size=640
+    python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 imgsz=640
 
     TODO:
     Direct cli support, i.e, yolov8 classify_train args.epochs 10