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`ultralytics 8.0.32` HUB and TensorFlow fixes (#870)

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docs/hub.md
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@ -1,30 +1,54 @@
# Ultralytics HUB
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<br>
<br>
<div align="center">
<a href="https://hub.ultralytics.com" target="_blank">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
<br>
<br>
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
</div>
<br>
[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.
<p align="center"><img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" /></p>
<p align="center">
<img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" />
</p>
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!
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/198611715-540c9856-49d7-4069-a2fd-7c9eb70e772e.png">
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">
## 2. Train a Model
Connect to the Ultralytics HUB notebook and use your model API key to begin
training! <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
Connect to the Ultralytics HUB notebook and use your model API key to begin training!
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
## 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)!
<a href="https://ultralytics.com/app_install" target="_blank">
<img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
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

2
ultralytics/__init__.py
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@ -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

5
ultralytics/hub/session.py
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@ -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

1
ultralytics/hub/utils.py
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@ -190,5 +190,4 @@ class Traces:
# Run below code on hub/utils init -------------------------------------------------------------------------------------
traces = Traces()

14
ultralytics/yolo/cfg/__init__.py
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@ -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):

2
ultralytics/yolo/data/base.py
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@ -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

14
ultralytics/yolo/data/build.py
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@ -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,

14
ultralytics/yolo/data/dataset.py
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@ -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:
@ -22,11 +22,9 @@ class YOLODataset(BaseDataset):
prefix (str): prefix.
"""
def __init__(
self,
def __init__(self,
img_path,
imgsz=640,
label_path=None,
cache=False,
augment=True,
hyp=None,
@ -38,12 +36,12 @@ class YOLODataset(BaseDataset):
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

18
ultralytics/yolo/data/utils.py
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@ -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[:, 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]}"
assert (lb[:, 1:] <=
1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
_, 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)

9
ultralytics/yolo/engine/exporter.py
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@ -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
@ -234,6 +234,9 @@ 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)
debug = False
if debug:
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self._export_pb(s_model)
if tflite or edgetpu:

2
ultralytics/yolo/engine/validator.py
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@ -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

4
ultralytics/yolo/utils/downloads.py
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@ -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'):

2
ultralytics/yolo/v8/detect/train.py
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@ -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

3
ultralytics/yolo/v8/detect/val.py
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@ -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"],

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