Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
Home Page: https://roboflow.com/models
train-yolov8-object-detection-on-custom-dataset.ipynb
/content
Ultralytics YOLOv8.0.14 ๐ Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB)
yolo/engine/trainer: task=detect, mode=train, model=yolov8s.pt, data=/content/pb-and-players-4/data.yaml, epochs=25, patience=50, batch=16, imgsz=800, save=True, cache=False, device=, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, classes=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train2
Traceback (most recent call last):
File "/usr/local/bin/yolo", line 8, in
sys.exit(entrypoint())
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/cfg/init.py", line 218, in entrypoint
func(cfg)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/v8/detect/train.py", line 205, in train
model.train(**vars(cfg))
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/model.py", line 199, in train
self.trainer = self.TrainerClass(overrides=overrides)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/trainer.py", line 122, in init
self.data = check_dataset_yaml(self.data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/utils.py", line 190, in check_dataset_yaml
data = check_file(data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/utils/checks.py", line 226, in check_file
assert len(files), f'File not found: {file}' # assert file was found
AssertionError: File not found: /content/pb-and-players-4/data.yaml
-Google Colab
download snippet:
!pip install roboflow
from roboflow import Roboflow
rf = Roboflow(api_key="######")
project = rf.workspace("halftone-digital").project("pb-and-players")
dataset = project.version(2).download("yolov8")
This worked just fine with the original version of my dataset, but subsequent versions throw this error:
link: https://app.roboflow.com/halftone-digital/pb-and-players/2#
train-yolov8-object-detection-on-custom-dataset.ipynb
https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb
When I run the training cell, I get the error in the image.
Google Colab
%cd {HOME}
!yolo task=detect mode=train model=yolov8s.pt data={dataset.location}/data.yaml epochs=25 imgsz=416
my ipynb link: https://colab.research.google.com/drive/1VyZulDnFdyZmz613v-nVc8gYuNCToz7s?usp=share_link
Train a mdoel to look for the healty cultures ( many others aspect can be studied )
No response
train-yolos-huggingface-object-detection-on-custom-data.ipynb
I am using my own dataset from roboflow, exported in coco format.
At:
#Setup dataloader for training loop
from torch.utils.data import DataLoader
def collate_fn(batch):
pixel_values = [item[0] for item in batch]
encoding = feature_extractor.pad(pixel_values, return_tensors="pt")
labels = [item[1] for item in batch]
batch = {}
batch['pixel_values'] = encoding['pixel_values']
batch['labels'] = labels
return batch
train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1, shuffle=True)
val_dataloader = DataLoader(val_dataset, collate_fn=collate_fn, batch_size=1)
batch = next(iter(train_dataloader))I get error:
The `max_size` parameter is deprecated and will be removed in v4.26. Please specify in `size['longest_edge'] instead`.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-7-bd1a63ffeba0>](https://localhost:8080/#) in <module>
14 train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1, shuffle=True)
15 val_dataloader = DataLoader(val_dataset, collate_fn=collate_fn, batch_size=1)
---> 16 batch = next(iter(train_dataloader))
7 frames
[/usr/local/lib/python3.8/dist-packages/transformers/models/yolos/image_processing_yolos.py](https://localhost:8080/#) in resize(self, image, size, resample, data_format, **kwargs)
805 size = (size["height"], size["width"])
806 else:
--> 807 raise ValueError(
808 "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
809 f" {size.keys()}."
ValueError: Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got dict_keys(['shortest_edge']).
Colab
No response
No response
how-to-track-football-players.ipynb
/content/yolov5
detect: weights=['yolov5x.pt'], source=/content/clips/08fd33_4.mp4, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=coco, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 ๐ v7.0-89-g35d6d9f Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt to yolov5x.pt...
100% 166M/166M [00:05<00:00, 31.3MB/s]
Fusing layers...
YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients
Traceback (most recent call last):
File "detect.py", line 261, in
main(opt)
File "detect.py", line 256, in main
run(**vars(opt))
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "detect.py", line 111, in run
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
File "/content/yolov5/utils/dataloaders.py", line 254, in init
raise FileNotFoundError(f'{p} does not exist')
FileNotFoundError: /content/clips/08fd33_4.mp4 does not exist
the same as yours
No response
No response
Track and Count Vehicles with YOLOv8 + ByteTRACK + Supervision
Supervision library was updated from 0.1.0 to 0.2.0. It looks like there was enough changed that this broke the imports.
Google Colab
See screenshot in Bug section
I believe imports can be fixed using the following:
from supervision.draw.color import ColorPalette
from supervision.geometry.core import Point
from supervision.video import VideoInfo, get_video_frames_generator, VideoSink
from supervision.notebook.utils import show_frame_in_notebook
from supervision.detection.core import Detections, BoxAnnotator
from supervision.detection.line_counter import LineZone, LineZoneAnnotatortrain-yolov8-object-detection-on-custom-dataset.ipynb
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-19-cec2a1551afa>](https://localhost:8080/#) in <module>
----> 1 project.version(dataset.version).deploy(model_type="yolov8", model_path=f"/content/runs/detect/train")
3 frames
[/usr/local/lib/python3.8/dist-packages/roboflow/core/version.py](https://localhost:8080/#) in deploy(self, model_type, model_path)
311 # add logic to save torch state dict safely
312 if model_type == "yolov8":
--> 313 model = torch.load(model_path + "weights/best.pt")
314
315 class_names = []
[/usr/local/lib/python3.8/dist-packages/torch/serialization.py](https://localhost:8080/#) in load(f, map_location, pickle_module, weights_only, **pickle_load_args)
769 pickle_load_args['encoding'] = 'utf-8'
770
--> 771 with _open_file_like(f, 'rb') as opened_file:
772 if _is_zipfile(opened_file):
773 # The zipfile reader is going to advance the current file position.
[/usr/local/lib/python3.8/dist-packages/torch/serialization.py](https://localhost:8080/#) in _open_file_like(name_or_buffer, mode)
268 def _open_file_like(name_or_buffer, mode):
269 if _is_path(name_or_buffer):
--> 270 return _open_file(name_or_buffer, mode)
271 else:
272 if 'w' in mode:
[/usr/local/lib/python3.8/dist-packages/torch/serialization.py](https://localhost:8080/#) in __init__(self, name, mode)
249 class _open_file(_opener):
250 def __init__(self, name, mode):
--> 251 super(_open_file, self).__init__(open(name, mode))
252
253 def __exit__(self, *args):
FileNotFoundError: [Errno 2] No such file or directory: '/content/runs/detect/trainweights/best.pt'
Colab
No response
Hope this is not another oversight from me, but I'm stuck :(
/content
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt to yolov8s-seg.pt...
100% 22.8M/22.8M [00:00<00:00, 150MB/s]
Ultralytics YOLOv8.0.11 ๐ Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)
yolo/engine/trainer: task=segment, mode=train, model=yolov8s-seg.pt, data=/content/datasets/TicketDetection-3/data.yaml, epochs=15, patience=50, batch=16, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, hydra={'output_subdir': None, 'run': {'dir': '.'}}, v5loader=False, save_dir=runs/segment/train
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 87.0MB/s]
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 928 ultralytics.nn.modules.Conv [3, 32, 3, 2]
1 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2]
2 -1 1 29056 ultralytics.nn.modules.C2f [64, 64, 1, True]
3 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2]
4 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True]
5 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2]
6 -1 2 788480 ultralytics.nn.modules.C2f [256, 256, 2, True]
7 -1 1 1180672 ultralytics.nn.modules.Conv [256, 512, 3, 2]
8 -1 1 1838080 ultralytics.nn.modules.C2f [512, 512, 1, True]
9 -1 1 656896 ultralytics.nn.modules.SPPF [512, 512, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1]
12 -1 1 591360 ultralytics.nn.modules.C2f [768, 256, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1]
15 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1]
16 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1]
18 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1]
19 -1 1 590336 ultralytics.nn.modules.Conv [256, 256, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1]
21 -1 1 1969152 ultralytics.nn.modules.C2f [768, 512, 1]
22 [15, 18, 21] 1 2770931 ultralytics.nn.modules.Segment [1, 32, 128, [128, 256, 512]]
YOLOv8s-seg summary: 261 layers, 11790483 parameters, 11790467 gradients, 42.7 GFLOPs
Transferred 411/417 items from pretrained weights
optimizer: SGD(lr=0.01) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias
train: Scanning /content/datasets/TicketDetection-3/train/labels... 150 images, 0 backgrounds, 0 corrupt: 100% 150/150 [00:00<00:00, 1433.15it/s]
train: New cache created: /content/datasets/TicketDetection-3/train/labels.cache
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /content/datasets/TicketDetection-3/valid/labels... 14 images, 0 backgrounds, 0 corrupt: 100% 14/14 [00:00<00:00, 637.08it/s]
val: New cache created: /content/datasets/TicketDetection-3/valid/labels.cache
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/segment/train
Starting training for 15 epochs...
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
0% 0/10 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/usr/local/bin/yolo", line 8, in <module>
sys.exit(entrypoint())
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/cli.py", line 148, in entrypoint
cli(cfg)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/cli.py", line 84, in cli
func(cfg)
File "/usr/local/lib/python3.8/dist-packages/hydra/main.py", line 79, in decorated_main
return task_function(cfg_passthrough)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/v8/segment/train.py", line 153, in train
model.train(**cfg)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/model.py", line 203, in train
self.trainer.train()
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/trainer.py", line 185, in train
self._do_train(int(os.getenv("RANK", -1)), world_size)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/trainer.py", line 285, in _do_train
for i, batch in pbar:
File "/usr/local/lib/python3.8/dist-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 628, in __next__
data = self._next_data()
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
return self._process_data(data)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
data.reraise()
File "/usr/local/lib/python3.8/dist-packages/torch/_utils.py", line 543, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 58, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 58, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/base.py", line 179, in __getitem__
return self.transforms(self.get_label_info(index))
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 48, in __call__
data = t(data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 48, in __call__
data = t(data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 361, in __call__
i = self.box_candidates(box1=instances.bboxes.T,
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 375, in box_candidates
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
ValueError: operands could not be broadcast together with shapes (3,) (4,)
Google Colab
!nvidia-smi
import os
HOME = os.getcwd()
print(HOME)
!pip install ultralytics==8.0.11
from IPython import display
display.clear_output()
import ultralytics
ultralytics.checks()
from ultralytics import YOLO
from IPython.display import display, Image
!mkdir {HOME}/datasets
%cd {HOME}/datasets
!pip install roboflow
display.clear_output()
from roboflow import Roboflow
rf = Roboflow(api_key="#####")
project = rf.workspace("muni").project("ticketdetection")
dataset = project.version(3).download("yolov5")
%cd {HOME}
!yolo task=segment mode=train model=yolov8s-seg.pt data={dataset.location}/data.yaml epochs=15 imgsz=640
The other important error i think is:
ValueError: operands could not be broadcast together with shapes (3,) (4,)
Can someone help me?
How-to-train-yolov8-instance-segmentation-on-custom-dataset.ipynb
Traceback (most recent call last):
File "/usr/local/bin/yolo", line 8, in
sys.exit(entrypoint())
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/cfg/init.py", line 266, in entrypoint
getattr(model, mode)(**vars(cfg))
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/engine/model.py", line 214, in train
self.trainer.train()
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/engine/trainer.py", line 182, in train
self._do_train(int(os.getenv("RANK", -1)), world_size)
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/engine/trainer.py", line 301, in _do_train
self.loss, self.loss_items = self.criterion(preds, batch)
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/v8/segment/train.py", line 44, in criterion
return self.compute_loss(preds, batch)
File "/usr/local/lib/python3.9/dist-packages/ultralytics/yolo/v8/segment/train.py", line 85, in call
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 49 but got size 0 for tensor number 1 in the list.
Sentry is attempting to send 1 pending error messages
Waiting up to 2 seconds
-Google colab
-Python 3.9
%cd {HOME}
!yolo task=segment mode=train model=yolov8m-seg.pt data={dataset.location}/data.yaml epochs=25 imgsz=640
/content
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt to yolov8m-seg.pt...
100% 52.4M/52.4M [00:05<00:00, 10.6MB/s]
Ultralytics YOLOv8.0.28 ๐ Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
yolo/engine/trainer: task=segment, mode=train, model=yolov8m-seg.pt, data=/content/datasets/Planeat-food-Instance-Segmentation-7/data.yaml, epochs=25, patience=50, batch=16, imgsz=640, save=True, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/segment/train
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 843kB/s]
2023-04-27 15:34:30.199497: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-27 15:34:31.234140: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Overriding model.yaml nc=80 with nc=121
i have a use case where i need to count sea animals in a big aquarium when moving camera is moving and scanning the aqaurium. i think i belong to object tracking of some sort and i see you did it on static video, but i am looking for a solution when camera is moving in a big space and counting.
No response
how-to-track-and-count-vehicles-with-yolov8.ipynb
https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-and-count-vehicles-with-yolov8.ipynb#scrollTo=8KdBkOflo2xY&uniqifier=1
In the installation of ByteTrack Step it shows the following error :
"
-Google colab
%cd {HOME}/ByteTrack
import yolox
No response
Track and Count Vehicles with YOLOv8 Notebook
The code block that installs Bytetrack throws an error:
Building wheels for collected packages: onnx, onnx-simplifier, lap, filterpy
error: subprocess-exited-with-error
ร Building wheel for onnx (pyproject.toml) did not run successfully.
โ exit code: 1
โฐโ> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
Building wheel for onnx (pyproject.toml) ... error
ERROR: Failed building wheel for onnx
Building wheel for onnx-simplifier (setup.py) ... done
Created wheel for onnx-simplifier: filename=onnx_simplifier-0.3.5-py3-none-any.whl size=12877 sha256=998f36b8ec7c3e08b350b718e40975f291ff6e5fe7f79eb03f1e3aeaaf4bea22
Stored in directory: /root/.cache/pip/wheels/84/cf/3e/05eb10ebcca1d2d42e43dcc10f9e1bc6fdae624a757f1f4019
Building wheel for lap (setup.py) ... done
Created wheel for lap: filename=lap-0.4.0-cp39-cp39-linux_x86_64.whl size=1655029 sha256=d64c1a692e5dd5d092f2691247e154ba4e7e7444394beb7eea541ba2223b0456
Stored in directory: /root/.cache/pip/wheels/2f/8b/30/e7dd4f9dc44fb438381df571c9a6bddc35aafd1bf39c4f8911
Building wheel for filterpy (setup.py) ... done
Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=581d3e8b20c21a5873e80becbe752c4a52a0efa87cf11f7be66c7b83946244b6
Stored in directory: /root/.cache/pip/wheels/53/e6/de/a09ea01e923aaf88b9f8c7c44329e857b2c1a31901167e55e6
Successfully built onnx-simplifier lap filterpy
Failed to build onnx
ERROR: Could not build wheels for onnx, which is required to install pyproject.toml-based projects
Run all code blocks. When the execution gets to "Installing Bytetrack", it throws an error and execution stops.
I managed to work around this issue by changing onnx version to 1.9.0 in the requirements.txt file in the Bytetrack directory.
[...]
# verified versions
onnx==1.9.0
onnxruntime==1.8.0
onnx-simplifier==0.3.5
I am trying to use Bytetrack as my tracker, with detections coming from a custom-dataset-trained yolov5 model and it happens sometimes that some frame doesn't have any detection. In that case, the tracker fails to update. What could be some solution for that? Thank you in advance!
This is the link to the notebook I am working in https://colab.research.google.com/drive/1nsZmuryO2lBz41CTTTBozO1VfQriXkll?usp=sharing.
When running the custom training code I ran into the error in the title. I also attached an image of the error.
No response
No response
YOLOv5 PyTorch Object Detection
When using the Roboflow data import, the dataset name is not written to the path of the dataset test split in the data.yaml file.
Example:
test: ../test/images
train: dataset_name/train/images
val: dataset_name/valid/images
This can lead to problems, e.g. when trying to validate on the dataset test split (with --task test):
FileNotFoundError: test: /content/yolov5/test/images does not exist
It is no problem adding the dataset name to the test path in the data.yaml file, but an inexperienced user might not know how to do this.
I don't know if there is a reason behind not adding the dataset name to the test path, if not this might be a bug.
No response
No response
train-yolov8-object-detection-on-custom-dataset.ipynb
FileNotFoundError:
Dataset '/home/elin/model_training/yolov8/ClassAction-1/data.yaml' not found
Fully reproduce Jupyter code here:
from roboflow import Roboflow
rf = Roboflow(api_key=input("Please Provide API Key"))
project = rf.workspace("elinswork").project("classaction")
dataset = project.version(1).download("yolov8")
import os
from IPython.display import clear_output
if os.path.exists("ClassAction-1"):
clear_output()
print("Dataset already downloaded")
else:
clear_output()
print("Failed to download dataset due to invalid API key or network error")Dataset already downloaded
from ultralytics import checks
checks()Ultralytics YOLOv8.0.20 ๐ Python-3.9.13 torch-1.13.1+cu117 CUDA:0 (NVIDIA GeForce RTX 2060, 6144MiB)
Setup complete โ
(12 CPUs, 15.5 GB RAM, 137.0/1006.9 GB disk)
from ultralytics import YOLO
model = YOLO("yolov8s.pt")
model.train(
data=dataset.location+"/data.yaml",
imgsz=640,
batch=4,
epochs=100,
)Ultralytics YOLOv8.0.20 ๐ Python-3.9.13 torch-1.13.1+cu117 CUDA:0 (NVIDIA GeForce RTX 2060, 6144MiB)
๏ฟฝ[34m๏ฟฝ[1myolo/engine/trainer: ๏ฟฝ[0mtask=detect, mode=train, model=yolov8s.yaml, data=/home/elin/model_training/yolov8/ClassAction-1/data.yaml, epochs=100, patience=50, batch=4, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=False, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs/detect/train3
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/tmp/ipykernel_1976/1704976871.py in <module>
1 from ultralytics import YOLO
2 model = YOLO("yolov8s.pt")
----> 3 model.train(
4 data=dataset.location+"/data.yaml",
5 imgsz=640,
~/anaconda3/lib/python3.9/site-packages/ultralytics/yolo/engine/model.py in train(self, **kwargs)
201 overrides["resume"] = self.ckpt_path
202
--> 203 self.trainer = self.TrainerClass(overrides=overrides)
204 if not overrides.get("resume"): # manually set model only if not resuming
205 self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
~/anaconda3/lib/python3.9/site-packages/ultralytics/yolo/engine/trainer.py in __init__(self, cfg, overrides)
120 self.data = self.args.data
121 if self.data.endswith(".yaml"):
--> 122 self.data = check_det_dataset(self.data)
123 elif self.args.task == 'classify':
124 self.data = check_cls_dataset(self.data)
~/anaconda3/lib/python3.9/site-packages/ultralytics/yolo/data/utils.py in check_det_dataset(dataset, autodownload)
235 LOGGER.warning(msg)
236 else:
--> 237 raise FileNotFoundError(msg)
238 t = time.time()
239 if s.startswith('http') and s.endswith('.zip'): # URL
FileNotFoundError:
Dataset '/home/elin/model_training/yolov8/ClassAction-1/data.yaml' not found โ ๏ธ, missing paths ['/home/elin/model_training/yolov8/datasets/ClassAction-1/valid/images']
!tree -L 2.
โโโ ClassAction-1
โย ย โโโ README.dataset.txt
โย ย โโโ README.roboflow.txt
โย ย โโโ data.yaml
โย ย โโโ test
โย ย โโโ train
โย ย โโโ valid
โโโ runs
โย ย โโโ detect
โโโ train.ipynb
โโโ yolov8s.pt
6 directories, 5 files
No response
Any ultralytics
The instructions currently are wrong. Correct are:
All
NA
NA
Roboflow How to Train SegFormer.ipynb
i just copy the code and try it but it wont work.
-Google Colab
No response
No response
Track and Count Vehicles with YOLOv8 + ByteTRACK + Supervision
Error when running the ByteTrack installation cell. 
%cd {HOME}
!git clone https://github.com/ifzhang/ByteTrack.git
%cd {HOME}/ByteTrack
!sed -i 's/onnx==1.8.1/onnx==1.9.0/g' requirements.txt
!pip3 install -q -r requirements.txt
!python3 setup.py -q develop
!pip install -q cython_bbox
!pip install -q onemetric
from IPython import display
display.clear_output()
import sys
sys.path.append(f"{HOME}/ByteTrack")
import yolox
print("yolox.version:", yolox.version)
No response
This issue affects the entire repo, not any single notebook.
Looks like the CONTRIBUTING file references a LICENSE.md file in another repo:
Lines 19 to 21 in acfb02a
but it would be helpful if this repo were self-contained and included its own license file, which would also let GitHub add a license badge to the repo to clearly specify the license.
N/A
N/A
No response
train-yolov6-object-detection-on-custom-data.ipynb
While executing
!pip install "roboflow>=0.2.11"
from roboflow import Roboflow
rf = Roboflow(api_key="chess-sample-only-api-key")
project = rf.workspace("joseph-nelson").project("chess-pieces-new")
dataset = project.version(23).download("mt-yolov6")
I get
__init__() missing 1 required positional argument: 'public'
Colab
No response
No response
train-yolov4-tiny-object-detection-on-custom-data.ipynb
Installing Darknet for YOLOv4 on Colab:
#install environment from the Makefile:
compilation terminated due to -Wfatal-errors. make: *** [Makefile:162: obj/convolutional_layer.o] Error 1
Google Colab
#install environment from the Makefile
%cd /content/darknet/
#!sed -i 's/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= -gencode arch=compute_75,code=sm_75/g' Makefile
#install environment from the Makefile
#note if you are on Colab Pro this works on a P100 GPU
#if you are on Colab free, you may need to change the Makefile for the K80 GPU
#this goes for any GPU, you need to change the Makefile to inform darknet which GPU you are running on.
!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
!sed -i 's/GPU=0/GPU=1/g' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile
!sed -i "s/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= ${ARCH_VALUE}/g" Makefile
!make
when the you get make: *** [Makefile:162: obj/convolutional_layer.o] Error 1 you can not Train Custom YOLOv4 Detector !./darknet detector train data/obj.data cfg/custom-yolov4-tiny-detector.cfg yolov4-tiny.conv.29 -dont_show -map
test
test
test
No response
No response
Hi first of all thanks for such a good notebook and a follow along youtube tutorial.
I was thinking of the possibility to reuse the model for smaller sports like doubles tennis etc.
Originally posted by yashathalye January 22, 2023
Saw the Track & Count Objects using YOLOv8 ByteTrack & Supervision video and tried with my use case but i am getting the error as "Bounding boxes batch must be defined as 2d np.array with (N, 4) shape, [] given" in the tqdm section your help is much obliged thank you
zero-shot-object-detection-with-grounding-dino.ipynb
No response
The issue happens because shutil.copyfile() attempts to open tempfile which is already opened. On Windows platform this raises Permission Error.
Here's how I could solve that in slconfig.py (lines 80-83):
80 with tempfile.TemporaryDirectory() as temp_config_dir:
81 temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py") # here temp_config_file is opened
82 temp_config_name = osp.basename(temp_config_file.name)
# on Windows attempt to open temp_config_file by shutil raises IOError: [Errno 13] Permission denied:
# close file before using shutil
temp_config_file.close()
83 shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
/bin/bash: ./darknet: No such file or directory
!./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg yolov4.conv.137 -dont_show -map
#If you get CUDA out of memory adjust subdivisions above!
#adjust max batches down for shorter training above
No response
how-to-track-football-players.ipynb
Hi, I was running
%cd {HOME}
!kaggle competitions files -c dfl-bundesliga-data-shootout | \
grep clips | head -20 | \
awk '{print $1}' | \
while read -r line; \
do kaggle competitions download -c dfl-bundesliga-data-shootout -f $line -p clips --quiet; \
unzip ${line}.zip -d clips && rm ${line}.zip; \
done
and got this error
403 - Forbidden unzip: cannot find or open clips/0a2d9b_1.mp4.zip
And when I visit the Kaggle competition site at https://www.kaggle.com/competitions/dfl-bundesliga-data-shootout/rules, it says:
New entrants are currently not allowed. You will be able to accept the rules and submit late predictions after the competition completes.
Is it because the dataset is currently locked?
No response
No response
Roboflow-TensorFlow2-Object-Detection.ipynb
https://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD#scrollTo=fF8ysCfYKgTP&uniqifier=1
No response
Many of Roboflow's notebooks are errors like EfficientDet, FasterRCNN, and MobileNetSSDv2. Please update all of your notebooks. Thanks
Hi @hansent @tonylampada @yeldarby @RobertoNovelo ,
Thanks for the great work on image annotation. I was trying to filter out the bounding boxes by area on the output of the detections which is supervisior detection format. I was able to convert that into an numpy array. But, how to convert that numpy array back into supervisor detection class? Following is the small code :
# detect objects
detections = grounding_dino_model.predict_with_classes(
image=image,
classes=enhance_class_name(class_names=CLASSES),
box_threshold=BOX_TRESHOLD,
text_threshold=TEXT_TRESHOLD
)
#print(type(detections))
detections = np.array(detections)
det1 = []
for k in range(len(detections)):
x1,y1,x2,y2 = detections[k][0][0],detections[k][0][1],detections[k][0][2],detections[k][0][3]
x_diff = abs(x2-x1)
y_diff = abs(y2-y1)
if x_diff<250 and y_diff<150 :
det1.append(detections[k])
detections = np.array(det1)
# annotate image with detections
box_annotator = sv.BoxAnnotator()
labels = [
#f"{CLASSES[class_id]} {confidence:0.2f}"
f"{CLASSES[class_id]} {confidence:0.2f}" if class_id is not None else f"{'other'} {confidence:0.2f}"
for _, _, confidence, class_id, _
in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)Any suggestions would be highly useful.
what changes must be made in order to use this tracker with another version of YOLO like YOLOv5?
I'll be so grateful if somebody helps
No response
how-to-track-football-players.ipynb
I'm trying to run the model best.pt located here using the following command:
%cd {HOME}/yolov5
!python detect.py --weights best.pt --img 1280 720 --conf 0.25 --source {HOME}/clipcut.mp4 --name custom
/content/yolov5
detect: weights=['best.pt'], source=/content/clipcut.mp4, data=data/coco128.yaml, imgsz=[1280, 720], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=custom, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 ๐ v7.0-30-g342fe05 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)
Traceback (most recent call last):
File "detect.py", line 261, in <module>
main(opt)
File "detect.py", line 256, in main
run(**vars(opt))
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "detect.py", line 98, in run
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
File "/content/yolov5/models/common.py", line 345, in __init__
model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
File "/content/yolov5/models/experimental.py", line 79, in attempt_load
ckpt = torch.load(attempt_download(w), map_location='cpu') # load
File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 777, in load
with _open_zipfile_reader(opened_file) as opened_zipfile:
File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 282, in __init__
super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory
using the yolo5x.pt that comes in the yolo repo works fine:
!python detect.py --weights yolov5x.pt --img 1280 720 --conf 0.25 --source {HOME}/clipcut.mp4 --name coco
google colab
No response
No response
train-yolov8-instance-segmentation-on-custom-dataset
hello
i want to instance segmentation with yolov8 but i've got this error and i am stuck here
i use this command to run yolov8
!yolo task=segment mode=train model=yolov8m-seg.pt data={dataset.location}/data.yaml epochs=100 imgsz=1024 batch=8
my images width and height are 1024x1024
i get this error
Sizes of tensors must match except in dimension 1. Expected size 57 but got size 0 for tensor number 1 in the list.
Sentry is attempting to send 1 pending error messages
also when i change image sizes or batch size the number of expected size changed in error
google colab
No response
No response
as i see on your roboflow notebook i make a new directory and put that path into classification training 'data' but it still show error,,i tried even in my drive i also add a new directory then copy that path into classification training 'data' bt unfortunately it still show error,,plz solve this issue, this is the issue screen shot
and this is my directory where i keep my data:
plz plz solve this issue
I am getting an OS error and I can't seem to understand why I am getting it
Here is the jupyter notebook attached
YOLOv7 PyTorch Object Detection: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov7-object-detection-on-custom-data.ipynb
My version: https://colab.research.google.com/drive/1Ky1iXpECpx8HJAIvnAXNhnqVy3rFixOi?usp=sharing
YOLOv7 Notebook does not train with --device flag set to 0, 1, 2, or 3, but only when set to 'cpu'
This is a problem because when set to --device 'cpu', as the batch processing time becomes very long (close to an hour) - and the training is meant to run, and most efficient, on GPU. The --device flag is supposed to recognize the CUDA device. In this case, it is in fact --device 0, but this is what causes the train.py command to fail when you run it.
Here's a Loom video of the process, and walking through the lines of code the RuntimeError points out: https://www.loom.com/share/393ec489c1d54698ba9626c7e1be897b
Traceback (most recent call last):
File "train.py", line 616, in <module>
train(hyp, opt, device, tb_writer)
File "train.py", line 363, in train
loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size
File "/content/yolov7/utils/loss.py", line 585, in __call__
bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
File "/content/yolov7/utils/loss.py", line 759, in build_targets
from_which_layer = from_which_layer[fg_mask_inboxes]
RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
Google Colab Pro
NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2A100-SXM4-40GB# run this cell to begin training
%cd /content/yolov7
!python train.py --batch 16 --epochs 55 --data /content/yolov7/Face-Detection-15/data.yaml --weights 'yolov7_training.pt' --device 0
This is the error (and what was logged just before the error):
/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Model Summary: 415 layers, 37196556 parameters, 37196556 gradients, 105.1 GFLOPS
Transferred 557/566 items from yolov7_training.pt
Scaled weight_decay = 0.0005
Optimizer groups: 95 .bias, 95 conv.weight, 98 other
train: Scanning 'Face-Detection-15/train/labels.cache' images and labels... 2871 found, 0 missing, 539 empty, 0 corrupted: 100% 2871/2871 [00:00<?, ?it/s]
val: Scanning 'Face-Detection-15/valid/labels.cache' images and labels... 267 found, 0 missing, 50 empty, 0 corrupted: 100% 267/267 [00:00<?, ?it/s]
autoanchor: Analyzing anchors... anchors/target = 4.67, Best Possible Recall (BPR) = 0.9981
Image sizes 640 train, 640 test
Using 8 dataloader workers
Logging results to runs/train/exp4
Starting training for 55 epochs...
Epoch gpu_mem box obj cls total labels img_size
0% 0/180 [00:07<?, ?it/s]
Traceback (most recent call last):
File "train.py", line 616, in <module>
train(hyp, opt, device, tb_writer)
File "train.py", line 363, in train
loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size
File "/content/yolov7/utils/loss.py", line 585, in __call__
bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
File "/content/yolov7/utils/loss.py", line 759, in build_targets
from_which_layer = from_which_layer[fg_mask_inboxes]
RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
YOLO v8 Notebook -
https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb
After collecting the football dataset successfully when I am running the custom training, it is throwing a 'dataset not found' error in both Kaggle and colab
No response
No response
The YOLOv5 Pytorch object detection notebook fails to open
Google Chrome / Colab
No response
No response
https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolo-nas-on-custom-dataset.ipynb
I got below error at "Inference with trained model" part:
Google Colab
ds = sv.Dataset.from_yolo(
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml",
force_masks=False
)No response
PermissionError Traceback (most recent call last)
Cell In[46], line 1
----> 1 from roboflow import Roboflow
3 # this is from the roboflow public dataset - they have all sort of datasets to work with Yolov5
4 # so we went with it to make our work easier
File :991, in find_and_load(name, import)
File :971, in find_and_load_unlocked(name, import)
File :914, in _find_spec(name, path, target)
File :1407, in find_spec(cls, fullname, path, target)
File :1376, in _get_spec(cls, fullname, path, target)
File :1340, in _path_importer_cache(cls, path)
PermissionError: [Errno 1] Operation not permitted
I'm using a M2 Pro MACBOOK with pytorch environment managed by anaconda. I found this:
roboflow 0.2.29 pypi_0 pypi
Thanks!
The evaluation code block in your notebook reads the valid_ds and use iterators to extract inputs and labels to predict the classes further. I want to test my model on run time for which I do not have any labels. For eg: I have 5 images of digits 1, 2, 3, 4, 5 respectively (I know labels because I can see them, no written csv label files).
How will I do that?
No response
YOLO-NAS Object Detection
Caching annotations: 100%|โโโโโโโโโโ| 2877/2877 [00:01<00:00, 2005.08it/s]
---------------------------------------------------------------------------
EmptyDatasetException Traceback (most recent call last)
[<ipython-input-18-fd6cc5dc80cf>](https://localhost:8080/#) in <cell line: 4>()
2 coco_detection_yolo_format_train, coco_detection_yolo_format_val)
3
----> 4 train_data = coco_detection_yolo_format_train(
5 dataset_params={
6 'data_dir': dataset_params['data_dir'],
5 frames
[/usr/local/lib/python3.10/dist-packages/super_gradients/training/datasets/detection_datasets/detection_dataset.py](https://localhost:8080/#) in _cache_annotations(self)
214
215 if len(annotations) == 0:
--> 216 raise EmptyDatasetException(
217 f"Out of {self.n_available_samples} images, not a single one was found with any of these classes: {self.class_inclusion_list}"
218 )
EmptyDatasetException: Empty Dataset: Out of 2877 images, not a single one was found with any of these classes: None
No response
Getting error when try to run
from super_gradients.training.dataloaders.dataloaders import (
coco_detection_yolo_format_train, coco_detection_yolo_format_val)
train_data = coco_detection_yolo_format_train(
dataset_params={
'data_dir': dataset_params['data_dir'],
'images_dir': dataset_params['train_images_dir'],
'labels_dir': dataset_params['train_labels_dir'],
'classes': dataset_params['classes']
},
dataloader_params={
'batch_size': BATCH_SIZE,
'num_workers': 2
}
)
val_data = coco_detection_yolo_format_val(
dataset_params={
'data_dir': dataset_params['data_dir'],
'images_dir': dataset_params['val_images_dir'],
'labels_dir': dataset_params['val_labels_dir'],
'classes': dataset_params['classes']
},
dataloader_params={
'batch_size': BATCH_SIZE,
'num_workers': 2
}
)
test_data = coco_detection_yolo_format_val(
dataset_params={
'data_dir': dataset_params['data_dir'],
'images_dir': dataset_params['test_images_dir'],
'labels_dir': dataset_params['test_labels_dir'],
'classes': dataset_params['classes']
},
dataloader_params={
'batch_size': BATCH_SIZE,
'num_workers': 2
}
)
cell on notebook.
Hi! I tried to train instance segmentation on a custom dataset with roboflow notebook but when I started training part it throw runtime error. please help me to fix this error. I tried the dataset in my local machine also. the same error is showing up. Whats wrong with the dataset?
Dataset
No response
I only shared to [email protected] email
Play together-train-yolov8-object-detection-on-custom-dataset.ipynb
https://colab.research.google.com/drive/1vuPjCsAlz6Tq3xpvkkhu1KfcXmylWqqH?usp=sharing
When I tried to detect objects in the video clips, it seems that It did not frame the objects properly. However, when I uploaded an image, it successfully detected and framed the object. I am not sure what happened, as detecting and framing objects in videos worked fine before, but now it is not working anymore.
Detect and framed as image is successful
https://user-images.githubusercontent.com/9458891/229852419-0c270d1a-f01f-4801-9866-e75a3e12fda5.mp4
Detect and framed as video clip is failed
https://user-images.githubusercontent.com/9458891/229853154-91307cc1-44ae-4445-88fe-3acc51be3e31.mp4
Google Colab
No response
I only shared to [email protected] email
Testing image file
https://drive.google.com/file/d/1KFIdnzsPTlhk2JV28PnlKj789TmMXopp/view?usp=sharing
Testing video clip
https://drive.google.com/file/d/1dZVyWttWDBSkdm_xLtIjI3ZzyuqGfAe1/view?usp=sharing
Trained model file
https://drive.google.com/file/d/10E-iI6Go4NVt6YpWKJAb3-NLqzixrEFn/view?usp=sharing
Here's the link that I posted few weeks ago. (sorry been busy, wasn't got a time to write issue over in Github)
https://discuss.roboflow.com/t/unable-to-detect-and-framed-the-objects-when-using-video-clip/1730
how-to-track-and-count-vehicles-with-yolov8
Google colab
No response
No response
in detection and segmentation 'custom training' i use (data='data.yaml'), see in the image below 
but in classification 'custom training' i use (data='data.yaml') bt get the error, see in the image below
i your notebook u tell data=dataset.location bt i dont understand where did i get dataset.location ?
train-segformer-segmentation-on-custom-data.ipynb
The notebook exports the baloon dataset as:
dataset = project.version(1).download("png-mask-semantic")
However on my custom dataset on Roboflow the only available semantic segmentation is coco-segmentation. I am not sure if this is specific to my dataset or a general issue?
Colab
No response
No response
hi,
will there also be a Colab notebook for a TFlite model in the future?
No response
In the "how-to-track-and-count-vehicles-with-yolov8.ipynb", when I run this block, I met an error:
`
generator = get_video_frames_generator(SOURCE_VIDEO_PATH)
box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)
iterator = iter(generator)
frame = next(iterator)
results = model(frame)
detections = Detections(
xyxy=results[0].boxes.xyxy.cpu().numpy(),
confidence=results[0].boxes.conf.cpu().numpy(),
class_id=results[0].boxes.cls.cpu().numpy().astype(int)
)
labels = [
f"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
for _, confidence, class_id, tracker_id
in detections
]
frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
%matplotlib inline
show_frame_in_notebook(frame, (16, 16))
`
ImportError Traceback (most recent call last)
in <cell line: 9>()
7 frame = next(iterator)
8 # model prediction on single frame and conversion to supervision Detections
----> 9 results = model(frame)
10 detections = Detections(
11 xyxy=results[0].boxes.xyxy.cpu().numpy(),
5 frames
/usr/local/lib/python3.10/dist-packages/ultralytics/yolo/engine/model.py in call(self, source, stream, **kwargs)
109 self.model.info(verbose=verbose)
110
--> 111 def fuse(self):
112 self.model.fuse()
113
/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
116
117 return decorate_context
/usr/local/lib/python3.10/dist-packages/ultralytics/yolo/engine/model.py in predict(self, source, stream, **kwargs)
/usr/local/lib/python3.10/dist-packages/ultralytics/yolo/engine/predictor.py in init(self, cfg, overrides, _callbacks)
107
108 def setup_source(self, source=None):
--> 109 if not self.model:
110 raise Exception("setup model before setting up source!")
111 # source
/usr/local/lib/python3.10/dist-packages/ultralytics/yolo/utils/callbacks/base.py in add_integration_callbacks(instance)
/usr/local/lib/python3.10/dist-packages/ultralytics/yolo/utils/callbacks/hub.py in
4 from time import time
5
----> 6 from ultralytics.hub.utils import PREFIX, sync_analytics
7 from ultralytics.yolo.utils import LOGGER
8
ImportError: cannot import name 'sync_analytics' from 'ultralytics.hub.utils' (/usr/local/lib/python3.10/dist-packages/ultralytics/hub/utils.py)
google colab
No response
No response
zero-shot-object-detection-with-grounding-dino.ipynb
By just running the notebook on colab i get the following error: "module 'supervision' has no attribute 'show_frame_in_notebook'"
Google Colab
Just run the standard notebook on colab
No response
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