Open openedev opened 9 months ago
Hi Jagan,
thank you, I'll check it. I will provide a feedback by the weekend at the latest.
Best wishes, Bela
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 13., K, 14:54):
Hi,
I'm trying to follow your instruction to train the model for yolov5 but found below please check the same.
$ ls images welding welding_images.zip yolov5 $ rm -rf yolov5/data/images/ $ cp images/ yolov5/data/ -rf $ mkdir yolov5/data/labels $ cp yolov5/data/images/.txt yolov5/data/labels/ $ cp welding/yolov5files/autosplit yolov5/data/ $ cp welding/yolov5_files/welding_data.yaml yolov5/ $ cp welding/yolov5_files/hyp.scratch-* yolov5/data/hyps/
Here, is I'm training.
$ cd yolov5 $ python train.py --cos-lr --img 640 --batch 32 --epochs 200 --data welding_data.yaml --weights yolov5n.pt --project defects --name model_5n_dec4 --cache --freeze 10 2024-02-13 13:46:19.973283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-02-13 13:46:19.973332: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-02-13 13:46:19.973363: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered train: weights=yolov5n.pt, cfg=, data=welding_data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=200, batch_size=32, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=defects, name=model_5n_dec4, exist_ok=False, quad=False, cos_lr=True, label_smoothing=0.0, patience=100, freeze=[10], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False github: up to date with https://github.com/ultralytics/yolov5 β YOLOv5 π v7.0-284-g95ebf68f Python-3.11.5 torch-2.2.0+cu121 CPU
hyperparameters: lr0=0.01, lrf=0.1, momentum=0.9, weight_decay=0.0005, warmup_epochs=0.3, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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 Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 π runs in Comet TensorBoard: Start with 'tensorboard --logdir defects', view at http://localhost:6006/ Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt to yolov5n.pt... 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.87M/3.87M [00:00<00:00, 7.28MB/s]
Overriding model.yaml nc=80 with nc=7
from n params module arguments
0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 models.common.Conv [16, 32, 3, 2] 2 -1 1 4800 models.common.C3 [32, 32, 1] 3 -1 1 18560 models.common.Conv [32, 64, 3, 2] 4 -1 2 29184 models.common.C3 [64, 64, 2] 5 -1 1 73984 models.common.Conv [64, 128, 3, 2] 6 -1 3 156928 models.common.C3 [128, 128, 3] 7 -1 1 295424 models.common.Conv [128, 256, 3, 2] 8 -1 1 296448 models.common.C3 [256, 256, 1] 9 -1 1 164608 models.common.SPPF [256, 256, 5] 10 -1 1 33024 models.common.Conv [256, 128, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 90880 models.common.C3 [256, 128, 1, False] 14 -1 1 8320 models.common.Conv [128, 64, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 22912 models.common.C3 [128, 64, 1, False] 18 -1 1 36992 models.common.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 74496 models.common.C3 [128, 128, 1, False] 21 -1 1 147712 models.common.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 296448 models.common.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 16236 models.yolo.Detect [7, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]] Model summary: 214 layers, 1773388 parameters, 1773388 gradients, 4.3 GFLOPs
Transferred 343/349 items from yolov5n.pt freezing model.0.conv.weight freezing model.0.bn.weight freezing model.0.bn.bias freezing model.1.conv.weight freezing model.1.bn.weight freezing model.1.bn.bias freezing model.2.cv1.conv.weight freezing model.2.cv1.bn.weight freezing model.2.cv1.bn.bias freezing model.2.cv2.conv.weight freezing model.2.cv2.bn.weight freezing model.2.cv2.bn.bias freezing model.2.cv3.conv.weight freezing model.2.cv3.bn.weight freezing model.2.cv3.bn.bias freezing model.2.m.0.cv1.conv.weight freezing model.2.m.0.cv1.bn.weight freezing model.2.m.0.cv1.bn.bias freezing model.2.m.0.cv2.conv.weight freezing model.2.m.0.cv2.bn.weight freezing model.2.m.0.cv2.bn.bias freezing model.3.conv.weight freezing model.3.bn.weight freezing model.3.bn.bias freezing model.4.cv1.conv.weight freezing model.4.cv1.bn.weight freezing model.4.cv1.bn.bias freezing model.4.cv2.conv.weight freezing model.4.cv2.bn.weight freezing model.4.cv2.bn.bias freezing model.4.cv3.conv.weight freezing model.4.cv3.bn.weight freezing model.4.cv3.bn.bias freezing model.4.m.0.cv1.conv.weight freezing model.4.m.0.cv1.bn.weight freezing model.4.m.0.cv1.bn.bias freezing model.4.m.0.cv2.conv.weight freezing model.4.m.0.cv2.bn.weight freezing model.4.m.0.cv2.bn.bias freezing model.4.m.1.cv1.conv.weight freezing model.4.m.1.cv1.bn.weight freezing model.4.m.1.cv1.bn.bias freezing model.4.m.1.cv2.conv.weight freezing model.4.m.1.cv2.bn.weight freezing model.4.m.1.cv2.bn.bias freezing model.5.conv.weight freezing model.5.bn.weight freezing model.5.bn.bias freezing model.6.cv1.conv.weight freezing model.6.cv1.bn.weight freezing model.6.cv1.bn.bias freezing model.6.cv2.conv.weight freezing model.6.cv2.bn.weight freezing model.6.cv2.bn.bias freezing model.6.cv3.conv.weight freezing model.6.cv3.bn.weight freezing model.6.cv3.bn.bias freezing model.6.m.0.cv1.conv.weight freezing model.6.m.0.cv1.bn.weight freezing model.6.m.0.cv1.bn.bias freezing model.6.m.0.cv2.conv.weight freezing model.6.m.0.cv2.bn.weight freezing model.6.m.0.cv2.bn.bias freezing model.6.m.1.cv1.conv.weight freezing model.6.m.1.cv1.bn.weight freezing model.6.m.1.cv1.bn.bias freezing model.6.m.1.cv2.conv.weight freezing model.6.m.1.cv2.bn.weight freezing model.6.m.1.cv2.bn.bias freezing model.6.m.2.cv1.conv.weight freezing model.6.m.2.cv1.bn.weight freezing model.6.m.2.cv1.bn.bias freezing model.6.m.2.cv2.conv.weight freezing model.6.m.2.cv2.bn.weight freezing model.6.m.2.cv2.bn.bias freezing model.7.conv.weight freezing model.7.bn.weight freezing model.7.bn.bias freezing model.8.cv1.conv.weight freezing model.8.cv1.bn.weight freezing model.8.cv1.bn.bias freezing model.8.cv2.conv.weight freezing model.8.cv2.bn.weight freezing model.8.cv2.bn.bias freezing model.8.cv3.conv.weight freezing model.8.cv3.bn.weight freezing model.8.cv3.bn.bias freezing model.8.m.0.cv1.conv.weight freezing model.8.m.0.cv1.bn.weight freezing model.8.m.0.cv1.bn.bias freezing model.8.m.0.cv2.conv.weight freezing model.8.m.0.cv2.bn.weight freezing model.8.m.0.cv2.bn.bias freezing model.9.cv1.conv.weight freezing model.9.cv1.bn.weight freezing model.9.cv1.bn.bias freezing model.9.cv2.conv.weight freezing model.9.cv2.bn.weight freezing model.9.cv2.bn.bias optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias train: Scanning /home/build/shared/airockchip/welding/yolov5/data/autosplit_train... 201 images, 0 backgrounds, 0 corrupt: 100%|ββββββββββ| 201/201 [00:00<00:00, 5021.02it/s] train: New cache created: /home/build/shared/airockchip/welding/yolov5/data/autosplit_train.cache train: Caching images (0.2GB ram): 100%|ββββββββββ| 201/201 [00:05<00:00, 35.55it/s] val: Scanning /home/build/shared/airockchip/welding/yolov5/data/autosplit_val... 56 images, 0 backgrounds, 0 corrupt: 100%|ββββββββββ| 56/56 [00:00<00:00, 382.35it/s] val: New cache created: /home/build/shared/airockchip/welding/yolov5/data/autosplit_val.cache ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm). ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm). ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm). ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm). Traceback (most recent call last): File "/home/build/conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "/home/build/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py", line 568, in reduce_storage fd, size = storage._share_fdcpu() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 294, in wrapper return fn(self, *args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 364, in _share_fdcpu return super()._share_fdcpu(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: unable to write to file : No space left on device (28) Traceback (most recent call last): File "/home/build/conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "/home/build/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py", line 568, in reduce_storage fd, size = storage._share_fdcpu() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 294, in wrapper return fn(self, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 364, in _share_fdcpu return super()._share_fdcpu(*args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: unable to write to file : No space left on device (28) Traceback (most recent call last): File "/home/build/conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "/home/build/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py", line 568, in reduce_storage fd, size = storage._share_fdcpu() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 294, in wrapper return fn(self, *args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 364, in _share_fdcpu return super()._share_fdcpu(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: unable to write to file : No space left on device (28) Traceback (most recent call last): File "/home/build/conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "/home/build/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py", line 568, in reduce_storage fd, size = storage._share_fdcpu() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 294, in wrapper return fn(self, *args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 364, in _share_fdcpu return super()._share_fdcpu(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: unable to write to file : No space left on device (28) Traceback (most recent call last): File "/home/build/conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) File "/home/build/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py", line 568, in reduce_storage fd, size = storage._share_fdcpu() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 294, in wrapper return fn(self, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/storage.py", line 364, in _share_fdcpu return super()._share_fdcpu(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: unable to write to file : No space left on device (28) Traceback (most recent call last): File "/home/build/shared/airockchip/welding/yolov5/train.py", line 836, in
main(opt) File "/home/build/shared/airockchip/welding/yolov5/train.py", line 616, in main train(opt.hyp, opt, device, callbacks) File "/home/build/shared/airockchip/welding/yolov5/train.py", line 272, in train val_loader = create_dataloader( ^^^^^^^^^^^^^^^^^^ File "/home/build/shared/airockchip/welding/yolov5/utils/dataloaders.py", line 177, in create_dataloader dataset = LoadImagesAndLabels( ^^^^^^^^^^^^^^^^^^^^ File "/home/build/shared/airockchip/welding/yolov5/utils/dataloaders.py", line 640, in init if cache_images == "ram" and not self.check_cache_ram(prefix=prefix): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/shared/airockchip/welding/yolov5/utils/dataloaders.py", line 664, in check_cache_ram im = cv2.imread(random.choice(self.im_files)) # sample image ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/shared/airockchip/welding/yolov5/utils/general.py", line 1205, in imread return cv2.imdecode(np.fromfile(filename, np.uint8), flags) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/build/conda/lib/python3.11/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler _error_if_any_worker_fails() RuntimeError: DataLoader worker (pid 1455) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit. Also, the pre-trained .pt from link https://drive.google.com/drive/folders/1LcIzjeB0gurHA7OGhjajesNJnojtb4EG?usp=sharing shows wrong result as I attached. [image: Uploading out.pngβ¦]
Any help? Jagan.
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Hi Bela,
Thanks for the quick response. Meanwhile, if possible could you send the input image that you tested with default yolov7 pt.
Thanks, Jagan.
Hi Jagan,
you're welcome. The full dataset link is available on the github page. This link-> https://drive.google.com/file/d/1GrHhiCdmRnXbXEyWrLGfGGD0eS3YwDUb/view?usp=sharing
BW, Bela
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 13., K, 16:14):
Hi Bela,
Thanks for the quick response. Meanwhile, if possible could you send the input image that you tested with default yolov7 pt https://drive.google.com/drive/folders/1LcIzjeB0gurHA7OGhjajesNJnojtb4EG?usp=sharing .
Thanks, Jagan.
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Hmm. not sure whats wrong. This is output of yolov7 pt from that drive. Hope you are able to open it?
It looks like the welding_data.yaml
file is not used.
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 13., K, 16:48):
Hmm. not sure whats wrong. This is output of yolov7 pt from that drive. Hope you are able to open it?
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I didn't use your code as it is failing during training so, I did your default pt and convert onnx
$ python export.py --weight ./best.pt
@szbela87 One question the training step you mentioned in README.md create .pt ?
python train.py --cos-lr --img 640 --batch 32 --epochs 200 --data welding_data.yaml --weights yolov5n.pt --project defects --name model_5n_dec4 --cache --freeze 10
Yep. It creates the pt files to the ./defects/model_5n_dec4/weights
directory. best.pt
are the best weights corresponding to the fitness
function.
The training starts from the pretrained yolov5n.pt
weights (pretrained on
the COCO dataset).
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 14., Sze, 11:33):
@szbela87 https://github.com/szbela87 One question the training step you mentioned in README.md create .pt ?
python train.py --cos-lr --img 640 --batch 32 --epochs 200 --data welding_data.yaml --weights yolov5n.pt --project defects --name model_5n_dec4 --cache --freeze 10
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It creates defects/model_5n_dec4 and defects/model_5n_dec42
The 42 creates pt but 4 doesn't create pt which one should I use.
$ ls defects/model_5n_dec4 -l
total 16
-rw-r--r-- 1 build build 88 Feb 14 10:19 events.out.tfevents.1707905986.tops-ThinkPad-E14-Gen-5.485.0
-rw-r--r-- 1 build build 370 Feb 14 10:19 hyp.yaml
-rw-r--r-- 1 build build 1027 Feb 14 10:19 opt.yaml
drwxr-xr-x 2 build build 4096 Feb 14 10:19 weights
$ ls defects/model_5n_dec4/weights/ -l
total 0
$
$ ls defects/model_5n_dec42/ -l
total 3324
-rw-r--r-- 1 build build 145283 Feb 14 12:00 confusion_matrix.png
-rw-r--r-- 1 build build 1645571 Feb 14 12:00 events.out.tfevents.1707906465.tops-ThinkPad-E14-Gen-5.16.0
-rw-r--r-- 1 build build 290842 Feb 14 12:00 F1_curve.png
-rw-r--r-- 1 build build 370 Feb 14 10:27 hyp.yaml
-rw-r--r-- 1 build build 194033 Feb 14 10:28 labels_correlogram.jpg
-rw-r--r-- 1 build build 119986 Feb 14 10:28 labels.jpg
-rw-r--r-- 1 build build 1028 Feb 14 10:27 opt.yaml
-rw-r--r-- 1 build build 277956 Feb 14 12:00 P_curve.png
-rw-r--r-- 1 build build 181916 Feb 14 12:00 PR_curve.png
-rw-r--r-- 1 build build 221281 Feb 14 12:00 R_curve.png
-rw-r--r-- 1 build build 59094 Feb 14 12:00 results.csv
-rw-r--r-- 1 build build 228081 Feb 14 12:00 results.png
drwxr-xr-x 2 build build 4096 Feb 14 12:33 weights
$ ls defects/model_5n_dec42/weights/ -l
total 7696
-rw-r--r-- 1 build build 3938664 Feb 14 12:00 best.pt
-rw-r--r-- 1 build build 3938664 Feb 14 12:00 last.pt
Yep. If you run it multiple times, it starts numbering them like this, for example as you write dec41 and dec42. So, you're no longer training the model ending with dec4. There are always two weight sets saved during the current training, one is the current state, the other is the best model so far (best.pt). Logically, the latter only changes if you find a better model than before.
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 14., Sze, 13:36):
It creates defects/model_5n_dec4 and defects/model_5n_dec42
The 42 creates pt but 4 doesn't create pt which one should I use.
$ ls defects/model_5n_dec4 -l total 16 -rw-r--r-- 1 build build 88 Feb 14 10:19 events.out.tfevents.1707905986.tops-ThinkPad-E14-Gen-5.485.0 -rw-r--r-- 1 build build 370 Feb 14 10:19 hyp.yaml -rw-r--r-- 1 build build 1027 Feb 14 10:19 opt.yaml drwxr-xr-x 2 build build 4096 Feb 14 10:19 weights $ ls defects/model_5n_dec4/weights/ -l total 0 $
$ ls defects/model_5n_dec42/ -l total 3324 -rw-r--r-- 1 build build 145283 Feb 14 12:00 confusion_matrix.png -rw-r--r-- 1 build build 1645571 Feb 14 12:00 events.out.tfevents.1707906465.tops-ThinkPad-E14-Gen-5.16.0 -rw-r--r-- 1 build build 290842 Feb 14 12:00 F1_curve.png -rw-r--r-- 1 build build 370 Feb 14 10:27 hyp.yaml -rw-r--r-- 1 build build 194033 Feb 14 10:28 labels_correlogram.jpg -rw-r--r-- 1 build build 119986 Feb 14 10:28 labels.jpg -rw-r--r-- 1 build build 1028 Feb 14 10:27 opt.yaml -rw-r--r-- 1 build build 277956 Feb 14 12:00 P_curve.png -rw-r--r-- 1 build build 181916 Feb 14 12:00 PR_curve.png -rw-r--r-- 1 build build 221281 Feb 14 12:00 R_curve.png -rw-r--r-- 1 build build 59094 Feb 14 12:00 results.csv -rw-r--r-- 1 build build 228081 Feb 14 12:00 results.png drwxr-xr-x 2 build build 4096 Feb 14 12:33 weights $ ls defects/model_5n_dec42/weights/ -l total 7696 -rw-r--r-- 1 build build 3938664 Feb 14 12:00 best.pt -rw-r--r-- 1 build build 3938664 Feb 14 12:00 last.pt
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Okay. How to run it for output?
Do you mean to make predictions? Please see the evaluation scripts and/or the original yolov5 documentation: https://github.com/ultralytics/yolov5
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 14., Sze, 14:46):
Okay. How to run it for output?
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Not prediction, If I give some defected welding image then it should give output image with marking the defected areas like you shows on README.
Yeah. This is called prediction... And again: pls look at the evaluation scripts. And also visit the original documentation. Study the github page thoroughly. The original documentation as well.
All the best.
Jagan Teki @.***> ezt Γrta (idΕpont: 2024. febr. 14., Sze, 15:01):
Not prediction, If I give some defected welding image then it should give output image with marking the defected areas like you shows on README.
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Hi,
I'm trying to follow your instruction to train the model for yolov5 but found below please check the same.
Here, is I'm training.
Also, the pre-trained .pt from link shows wrong result as I attached. ![Uploading out.pngβ¦]()
Any help? Jagan.