Hello there,
I’m trying to train a YOLO 11 detector with COCO128 dataset, however it seems something is wrong when running the training session along with Comet ML as it gives some kind of error. If I disable Comet ML, it works as intended. I tried with YOLO 8 as well, same issue.
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
results = model.train(
data="coco128.yaml",
project="comet-example-yolov8-coco128",
batch=32,
save_period=1,
save_json=True,
epochs=3
)
I’m getting the following output:
Ultralytics 8.3.64 Python-3.11.4 torch-2.5.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3060 Laptop GPU, 6144MiB)
engine\trainer: task=detect, mode=train, model=yolo11n.pt, data=coco128.yaml, epochs=3, time=None, patience=100, batch=32, imgsz=640, save=True, save_period=1, cache=False, device=None, workers=8, project=comet-example-yolov8-coco128, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=True, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, 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, pose=12.0, kobj=1.0, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=comet-example-yolov8-coco128\train3
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25]
3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25]
5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]
23 [16, 19, 22] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLO11n summary: 319 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs
Transferred 499/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed
train: Scanning D:\ml\yolo11code\datasets\coco128\labels\train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|
val: Scanning D:\ml\yolo11code\datasets\coco128\labels\train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██
Plotting labels to comet-example-yolov8-coco128\train3\labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to comet-example-yolov8-coco128\train3
Starting training for 3 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/3 6.64G 1.16 1.346 1.212 536 640: 100%|██████████| 4/4 [00:04<00:00, 1.07
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 2/2 [00:05<0
all 128 929 0.644 0.597 0.674 0.507
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[7], line 1
----> 1 results = model.train(
2 data="coco128.yaml",
3 project="comet-example-yolov8-coco128",
4 batch=32,
5 save_period=1,
6 save_json=True,
7 epochs=3
8 )
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\engine\model.py:806, in Model.train(self, trainer, **kwargs)
803 self.model = self.trainer.model
805 self.trainer.hub_session = self.session # attach optional HUB session
--> 806 self.trainer.train()
807 # Update model and cfg after training
808 if RANK in {-1, 0}:
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\engine\trainer.py:207, in BaseTrainer.train(self)
204 ddp_cleanup(self, str(file))
206 else:
--> 207 self._do_train(world_size)
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\engine\trainer.py:454, in BaseTrainer._do_train(self, world_size)
452 self.scheduler.last_epoch = self.epoch # do not move
453 self.stop |= epoch >= self.epochs # stop if exceeded epochs
--> 454 self.run_callbacks("on_fit_epoch_end")
455 self._clear_memory()
457 # Early Stopping
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\engine\trainer.py:168, in BaseTrainer.run_callbacks(self, event)
166 """Run all existing callbacks associated with a particular event."""
167 for callback in self.callbacks.get(event, []):
--> 168 callback(self)
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\utils\callbacks\comet.py:360, in on_fit_epoch_end(trainer)
358 _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
359 if _should_log_image_predictions():
--> 360 _log_image_predictions(experiment, trainer.validator, curr_step)
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\utils\callbacks\comet.py:263, in _log_image_predictions(experiment, validator, curr_step)
260 return
262 image_path = Path(image_path)
--> 263 annotations = _fetch_annotations(
264 img_idx,
265 image_path,
266 batch,
267 predictions_metadata_map,
268 class_label_map,
269 )
270 _log_images(
271 experiment,
272 [image_path],
273 curr_step,
274 annotations=annotations,
275 )
276 _comet_image_prediction_count += 1
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\utils\callbacks\comet.py:194, in _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map)
190 """Join the ground truth and prediction annotations if they exist."""
191 ground_truth_annotations = _format_ground_truth_annotations_for_detection(
192 img_idx, image_path, batch, class_label_map
193 )
--> 194 prediction_annotations = _format_prediction_annotations_for_detection(
195 image_path, prediction_metadata_map, class_label_map
196 )
198 annotations = [
199 annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None
200 ]
201 return [annotations] if annotations else None
File ~\AppData\Roaming\Python\Python311\site-packages\ultralytics\utils\callbacks\comet.py:182, in _format_prediction_annotations_for_detection(image_path, metadata, class_label_map)
180 cls_label = prediction["category_id"]
181 if class_label_map:
--> 182 cls_label = str(class_label_map[cls_label])
184 data.append({"boxes": [boxes], "label": cls_label, "score": score})
186 return {"name": "prediction", "data": data}
KeyError: 80