diff --git a/docs/hub.md b/docs/hub.md index f7a7dbb2..1ae00b1c 100644 --- a/docs/hub.md +++ b/docs/hub.md @@ -1,30 +1,54 @@ # Ultralytics HUB -
- - + + +

+
+ + + + + + + + + + + + + + + + + + + + +
+
CI CPU + + Open In Colab
- +
[Ultralytics HUB](https://hub.ultralytics.com) is a new no-code online tool developed by [Ultralytics](https://ultralytics.com), the creators of the popular [YOLOv5](https://github.com/ultralytics/yolov5) -object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLOv5 models +object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLO models without any coding or technical expertise. Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and select their model configurations. It also offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection and image segmentation tasks. Overall, -Ultralytics HUB is an essential tool for anyone looking to use YOLOv5 for their object detection and image segmentation +Ultralytics HUB is an essential tool for anyone looking to use YOLO for their object detection and image segmentation projects. **[Get started now](https://hub.ultralytics.com)** and experience the power and simplicity of Ultralytics HUB for -yourself. Sign up for a free account and -start building, training, and deploying YOLOv5 and YOLOv8 models today. +yourself. Sign up for a free account and start building, training, and deploying YOLOv5 and YOLOv8 models today. ## 1. Upload a Dataset @@ -44,7 +68,9 @@ zip -r coco6.zip coco6 The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be downloaded and unzipped to see exactly how to structure your custom dataset. -

+

+ +

The dataset YAML is the same standard YOLOv5 YAML format. See the [YOLOv5 Train Custom Data tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) for full details. @@ -68,20 +94,21 @@ names: After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab. Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it! -HUB Dataset Upload +HUB Dataset Upload ## 2. Train a Model -Connect to the Ultralytics HUB notebook and use your model API key to begin -training! Open In Colab +Connect to the Ultralytics HUB notebook and use your model API key to begin training! + + +Open In Colab ## 3. Deploy to Real World Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run -models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)! - - -Ultralytics mobile app +models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or +[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading +the [Ultralytics App](https://ultralytics.com/app_install)! ## ❓ Issues diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index a5841b5f..6520f00d 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, GPL-3.0 license -__version__ = "8.0.31" +__version__ = "8.0.32" from ultralytics.yolo.engine.model import YOLO from ultralytics.yolo.utils import ops diff --git a/ultralytics/hub/session.py b/ultralytics/hub/session.py index 883d06a9..f6e87855 100644 --- a/ultralytics/hub/session.py +++ b/ultralytics/hub/session.py @@ -12,7 +12,7 @@ from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_ from ultralytics.yolo.utils import is_colab, threaded, LOGGER, emojis, PREFIX from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params -AGENT_NAME = (f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local") +AGENT_NAME = f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local" session = None @@ -95,7 +95,8 @@ class HubTrainingSession: if data.get("status", None) == "trained": raise ValueError( - emojis(f"Model trained. View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")) + emojis(f"Model is already trained and uploaded to " + f"https://hub.ultralytics.com/models/{self.model_id} 🚀")) if not data.get("data", None): raise ValueError("Dataset may still be processing. Please wait a minute and try again.") # RF fix diff --git a/ultralytics/hub/utils.py b/ultralytics/hub/utils.py index eec139f1..f2cff500 100644 --- a/ultralytics/hub/utils.py +++ b/ultralytics/hub/utils.py @@ -190,5 +190,4 @@ class Traces: # Run below code on hub/utils init ------------------------------------------------------------------------------------- - traces = Traces() diff --git a/ultralytics/yolo/cfg/__init__.py b/ultralytics/yolo/cfg/__init__.py index ec1885fa..33a2e4cc 100644 --- a/ultralytics/yolo/cfg/__init__.py +++ b/ultralytics/yolo/cfg/__init__.py @@ -49,19 +49,19 @@ CLI_HELP_MSG = \ GitHub: https://github.com/ultralytics/ultralytics """ -CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl'} +CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear'} CFG_FRACTION_KEYS = { 'dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr', 'fl_gamma', - 'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'degrees', 'translate', 'scale', 'shear', 'perspective', 'flipud', - 'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou'} + 'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud', 'fliplr', 'mosaic', + 'mixup', 'copy_paste', 'conf', 'iou'} CFG_INT_KEYS = { 'epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride', 'line_thickness', 'workspace', 'nbs'} CFG_BOOL_KEYS = { - 'save', 'cache', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', - 'cos_lr', 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', - 'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', - 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'} + 'save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr', + 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', + 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras', + 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'} def cfg2dict(cfg): diff --git a/ultralytics/yolo/data/base.py b/ultralytics/yolo/data/base.py index 06347fa0..da321ccf 100644 --- a/ultralytics/yolo/data/base.py +++ b/ultralytics/yolo/data/base.py @@ -28,7 +28,6 @@ class BaseDataset(Dataset): self, img_path, imgsz=640, - label_path=None, cache=False, augment=True, hyp=None, @@ -42,7 +41,6 @@ class BaseDataset(Dataset): super().__init__() self.img_path = img_path self.imgsz = imgsz - self.label_path = label_path self.augment = augment self.single_cls = single_cls self.prefix = prefix diff --git a/ultralytics/yolo/data/build.py b/ultralytics/yolo/data/build.py index 3448232e..4cd59832 100644 --- a/ultralytics/yolo/data/build.py +++ b/ultralytics/yolo/data/build.py @@ -61,7 +61,7 @@ def seed_worker(worker_id): random.seed(worker_seed) -def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_path=None, rank=-1, mode="train"): +def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, rank=-1, mode="train"): assert mode in ["train", "val"] shuffle = mode == "train" if cfg.rect and shuffle: @@ -70,9 +70,8 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = YOLODataset( img_path=img_path, - label_path=label_path, imgsz=cfg.imgsz, - batch_size=batch_size, + batch_size=batch, augment=mode == "train", # augmentation hyp=cfg, # TODO: probably add a get_hyps_from_cfg function rect=cfg.rect or rect, # rectangular batches @@ -82,18 +81,19 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat pad=0.0 if mode == "train" else 0.5, prefix=colorstr(f"{mode}: "), use_segments=cfg.task == "segment", - use_keypoints=cfg.task == "keypoint") + use_keypoints=cfg.task == "keypoint", + names=names) - batch_size = min(batch_size, len(dataset)) + batch = min(batch, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices workers = cfg.workers if mode == "train" else cfg.workers * 2 - nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers]) # number of workers sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader # allow attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + RANK) return loader(dataset=dataset, - batch_size=batch_size, + batch_size=batch, shuffle=shuffle and sampler is None, num_workers=nw, sampler=sampler, diff --git a/ultralytics/yolo/data/dataset.py b/ultralytics/yolo/data/dataset.py index a6f52018..fc58f6c4 100644 --- a/ultralytics/yolo/data/dataset.py +++ b/ultralytics/yolo/data/dataset.py @@ -14,7 +14,7 @@ from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image class YOLODataset(BaseDataset): - cache_version = 1.0 # dataset labels *.cache version, >= 1.0 for YOLOv8 + cache_version = '1.0.1' # dataset labels *.cache version, >= 1.0.0 for YOLOv8 rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] """YOLO Dataset. Args: @@ -22,28 +22,26 @@ class YOLODataset(BaseDataset): prefix (str): prefix. """ - def __init__( - self, - img_path, - imgsz=640, - label_path=None, - cache=False, - augment=True, - hyp=None, - prefix="", - rect=False, - batch_size=None, - stride=32, - pad=0.0, - single_cls=False, - use_segments=False, - use_keypoints=False, - ): + def __init__(self, + img_path, + imgsz=640, + cache=False, + augment=True, + hyp=None, + prefix="", + rect=False, + batch_size=None, + stride=32, + pad=0.0, + single_cls=False, + use_segments=False, + use_keypoints=False, + names=None): self.use_segments = use_segments self.use_keypoints = use_keypoints + self.names = names assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints." - super().__init__(img_path, imgsz, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad, - single_cls) + super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls) def cache_labels(self, path=Path("./labels.cache")): # Cache dataset labels, check images and read shapes @@ -56,7 +54,7 @@ class YOLODataset(BaseDataset): with ThreadPool(NUM_THREADS) as pool: results = pool.imap(func=verify_image_label, iterable=zip(self.im_files, self.label_files, repeat(self.prefix), - repeat(self.use_keypoints))) + repeat(self.use_keypoints), repeat(len(self.names)))) pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT) for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: nm += nm_f diff --git a/ultralytics/yolo/data/utils.py b/ultralytics/yolo/data/utils.py index 91d8fe02..e9ec668a 100644 --- a/ultralytics/yolo/data/utils.py +++ b/ultralytics/yolo/data/utils.py @@ -61,7 +61,7 @@ def exif_size(img): def verify_image_label(args): # Verify one image-label pair - im_file, lb_file, prefix, keypoint = args + im_file, lb_file, prefix, keypoint, num_cls = args # number (missing, found, empty, corrupt), message, segments, keypoints nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None try: @@ -97,16 +97,20 @@ def verify_image_label(args): assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels" kpts = np.zeros((lb.shape[0], 39)) for i in range(len(lb)): - kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, - 3)) # remove the occlusion parameter from the GT + kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, 3)) # remove occlusion param from GT kpts[i] = np.hstack((lb[i, :5], kpt)) lb = kpts assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter" else: assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected" - assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" - assert (lb[:, 1:] <= - 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" + assert (lb[:, 1:] <= 1).all(), \ + f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" + # All labels + max_cls = int(lb[:, 0].max()) # max label count + assert max_cls <= num_cls, \ + f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \ + f'Possible class labels are 0-{num_cls - 1}' + assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" _, i = np.unique(lb, axis=0, return_index=True) if len(i) < nl: # duplicate row check lb = lb[i] # remove duplicates @@ -192,8 +196,8 @@ def check_det_dataset(dataset, autodownload=True): # Download (optional) extract_dir = '' if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): - download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1) - data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) + new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False) + data = next((DATASETS_DIR / new_dir).rglob('*.yaml')) extract_dir, autodownload = data.parent, False # Read yaml (optional) diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py index a70a68a3..442ae171 100644 --- a/ultralytics/yolo/engine/exporter.py +++ b/ultralytics/yolo/engine/exporter.py @@ -203,7 +203,7 @@ class Exporter: self.im = im self.model = model self.file = file - self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else (x.shape for x in y) + self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y) self.pretty_name = self.file.stem.replace('yolo', 'YOLO') self.metadata = { 'description': f"Ultralytics {self.pretty_name} model trained on {self.model.args['data']}", @@ -213,8 +213,8 @@ class Exporter: 'stride': int(max(model.stride)), 'names': model.names} # model metadata - LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and " - f"output shape {self.output_shape} ({file_size(file):.1f} MB)") + LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} BCHW and " + f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)") # Exports f = [''] * len(fmts) # exported filenames @@ -234,19 +234,22 @@ class Exporter: nms = False f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs, agnostic_nms=self.args.agnostic_nms or tfjs) - if pb or tfjs: # pb prerequisite to tfjs - f[6], _ = self._export_pb(s_model) - if tflite or edgetpu: - f[7], _ = self._export_tflite(s_model, - int8=self.args.int8 or edgetpu, - data=self.args.data, - nms=nms, - agnostic_nms=self.args.agnostic_nms) - if edgetpu: - f[8], _ = self._export_edgetpu() - self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape)) - if tfjs: - f[9], _ = self._export_tfjs() + + debug = False + if debug: + if pb or tfjs: # pb prerequisite to tfjs + f[6], _ = self._export_pb(s_model) + if tflite or edgetpu: + f[7], _ = self._export_tflite(s_model, + int8=self.args.int8 or edgetpu, + data=self.args.data, + nms=nms, + agnostic_nms=self.args.agnostic_nms) + if edgetpu: + f[8], _ = self._export_edgetpu() + self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape)) + if tfjs: + f[9], _ = self._export_tfjs() if paddle: # PaddlePaddle f[10], _ = self._export_paddle() diff --git a/ultralytics/yolo/engine/validator.py b/ultralytics/yolo/engine/validator.py index b3e8f587..e57f3bb0 100644 --- a/ultralytics/yolo/engine/validator.py +++ b/ultralytics/yolo/engine/validator.py @@ -120,7 +120,7 @@ class BaseValidator: if not pt: self.args.rect = False self.dataloader = self.dataloader or \ - self.get_dataloader(self.data.get("val") or self.data.set("test"), self.args.batch) + self.get_dataloader(self.data.get("val") or self.data.get("test"), self.args.batch) model.eval() model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup diff --git a/ultralytics/yolo/utils/downloads.py b/ultralytics/yolo/utils/downloads.py index b7b6d746..1a5f49e1 100644 --- a/ultralytics/yolo/utils/downloads.py +++ b/ultralytics/yolo/utils/downloads.py @@ -39,6 +39,7 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): for f in zipObj.namelist(): # list all archived filenames in the zip if all(x not in f for x in exclude): zipObj.extract(f, path=path) + return zipObj.namelist()[0] # return unzip dir def safe_download(url, @@ -112,13 +113,14 @@ def safe_download(url, unzip_dir = dir or f.parent # unzip to dir if provided else unzip in place LOGGER.info(f'Unzipping {f} to {unzip_dir}...') if f.suffix == '.zip': - unzip_file(file=f, path=unzip_dir) # unzip + unzip_dir = unzip_file(file=f, path=unzip_dir) # unzip elif f.suffix == '.tar': subprocess.run(['tar', 'xf', f, '--directory', unzip_dir], check=True) # unzip elif f.suffix == '.gz': subprocess.run(['tar', 'xfz', f, '--directory', unzip_dir], check=True) # unzip if delete: f.unlink() # remove zip + return unzip_dir def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'): diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py index c5b3a8b7..c199d228 100644 --- a/ultralytics/yolo/v8/detect/train.py +++ b/ultralytics/yolo/v8/detect/train.py @@ -41,7 +41,7 @@ class DetectionTrainer(BaseTrainer): shuffle=mode == "train", seed=self.args.seed)[0] if self.args.v5loader else \ build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode, - rect=mode == "val")[0] + rect=mode == "val", names=self.data['names'])[0] def preprocess_batch(self, batch): batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255 diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py index bc2148dc..f093b229 100644 --- a/ultralytics/yolo/v8/detect/val.py +++ b/ultralytics/yolo/v8/detect/val.py @@ -176,7 +176,8 @@ class DetectionValidator(BaseValidator): prefix=colorstr(f'{self.args.mode}: '), shuffle=False, seed=self.args.seed)[0] if self.args.v5loader else \ - build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0] + build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, names=self.data['names'], + mode="val")[0] def plot_val_samples(self, batch, ni): plot_images(batch["img"],