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View Code? Open in Web Editor NEWCustom Object Detection Training for DeepStack
License: GNU General Public License v3.0
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robmarkcole malenurse andreteixeira1998 raceland-automacao-lda therealmichaelberna codeproject shortpupper popdeveloper duncan1224deepstack-trainer's Issues
Add tensor board support
Training is taking surprisingly long, so would be useful to monitor metrics as we go
Lower epochs by default?
300 appears overkill if this is correct, although I have only a single class.
Add worked example with mAP optimisation to docs
File Not Found: data/hyp.scratch.yaml
Attempting to train with Firenet (yolo3 format I believe):
!wget https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/fire-dataset.zip -q
!unzip fire-dataset.zip
!python3 train.py --dataset-path "/content/deepstack-trainer/fire-dataset" --model yolov5s --batch-size 32 --classes "fire"
I get error:
Traceback (most recent call last):
File "train.py", line 496, in <module>
opt.cfg, opt.hyp = check_file(opt.cfg), check_file(opt.hyp) # check files
File "/content/deepstack-trainer/utils/general.py", line 70, in check_file
assert len(files), 'File Not Found: %s' % file # assert file was found
AssertionError: File Not Found: data/hyp.scratch.yaml
Add inference step after training
As its always nice to see. In my case performed with:
!python /content/deepstack-trainer/detect.py --weights /content/deepstack-trainer/train-runs/exp/weights/best.pt --img 640 --conf 0.25 --source /content/valid/images/
This batch processes all the images in /content/valid/images/.
Then the images are in a directory /content/runs/detect/exp
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.
I used your google colab.
Command: !python3 train.py --dataset-path "/content/test" --model "yolov5x"
Error:
Using torch 1.8.0+cu101 CUDA:0 (Tesla K80, 11441MB)
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5s.yaml', classes='', data={'train': '/content/test/train', 'val': '/content/test/test', 'nc': 3, 'names': ['Volkswagen', 'DeutschePost', 'DHL']}, dataset_path='/content/test', device='', epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, model='yolov5s', multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='train-runs/test', rect=False, resume=False, save_dir='train-runs/test/exp3', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)
Start Tensorboard with "tensorboard --logdir train-runs/test", view at http://localhost:6006/
2021-03-19 15:56:03.959323: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, '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}
Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...
100% 14.5M/14.5M [00:00<00:00, 22.4MB/s]
Overriding model.yaml nc=80 with nc=3
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 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 378624 models.common.BottleneckCSP [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 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 95104 models.common.BottleneckCSP [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Traceback (most recent call last):
File "train.py", line 530, in <module>
train(hyp, opt, device, tb_writer, wandb)
File "train.py", line 90, in train
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
File "/content/deepstack-trainer/deepstack-trainer/deepstack-trainer/deepstack-trainer/models/yolo.py", line 96, in __init__
self._initialize_biases() # only run once
File "/content/deepstack-trainer/deepstack-trainer/deepstack-trainer/deepstack-trainer/models/yolo.py", line 151, in _initialize_biases
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.
CUDA 11.1+ not compatible with any version of Torch that works with deepstack-trainer
The most recent functional Torch version that works with deepstack-trainer is 1.7.1 (without getting in place leaf node operation error that can be found in the resolved issues tab), which only supports Cuda 11.0. I was wondering if there is any solution to this. My GPU only functions with CUDA 11.1+.
Document classes.txt
I used Roboflow to import my yolo5 training data, and this doesn't include a classes.txt so I had to read the source to know what was required. We can just say it is a comma separated list of the classes?
NameError: name 'classes' is not defined
Getting a new error.
!python3 train.py --dataset-path "/content/train/" --model yolov5s --batch-size 32 --classes "fire"
Traceback (most recent call last):
File "train.py", line 471, in <module>
classnames = classes.split(",")
NameError: name 'classes' is not defined
Getting RuntimeError ....
Hi all,
I'm trying to train the custom model locally but have the following issue... Can someone tell me what I did wrong?
(base) C:\Utils\deepstack-trainer>python train.py --dataset-path C:\Movie\Deepstack\datasets\vehicles
Using torch 1.11.0 CUDA:0 (NVIDIA GeForce GTX 1650 Ti, 4095MB)
Namespace(model='yolov5m', classes='', dataset_path='C:\\Movie\\Deepstack\\datasets\\vehicles', hyp='data/hyp.scratch.yaml', epochs=300, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, log_imgs=16, workers=8, project='train-runs/vehicles', name='exp', exist_ok=False, cfg='.\\models\\yolov5m.yaml', weights='yolov5m.pt', data={'train': 'C:\\Movie\\Deepstack\\datasets\\vehicles\\train', 'val': 'C:\\Movie\\Deepstack\\datasets\\vehicles\\test', 'nc': 5, 'names': ['vehicle/car', 'vehicle/truck', 'vehicle/bus', 'vehicle/train', '']}, total_batch_size=16, world_size=1, global_rank=-1, save_dir='train-runs\\vehicles\\exp4')
Start Tensorboard with "tensorboard --logdir train-runs/vehicles", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, '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}
Overriding model.yaml nc=80 with nc=5
from n params module arguments
0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 1 67680 models.common.BottleneckCSP [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 1 639168 models.common.BottleneckCSP [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 1 2550144 models.common.BottleneckCSP [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 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 1219968 models.common.BottleneckCSP [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 305856 models.common.BottleneckCSP [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 1072512 models.common.BottleneckCSP [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
24 [17, 20, 23] 1 40410 models.yolo.Detect [5, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Traceback (most recent call last):
File "C:\Utils\deepstack-trainer\train.py", line 530, in <module>
train(hyp, opt, device, tb_writer, wandb)
File "C:\Utils\deepstack-trainer\train.py", line 90, in train
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
File "C:\Utils\deepstack-trainer\models\yolo.py", line 96, in __init__
self._initialize_biases() # only run once
File "C:\Utils\deepstack-trainer\models\yolo.py", line 151, in _initialize_biases
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.
I tried to use older version of PyTorch, as marcokloeckler suggested,
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
but got another error:
python train.py --dataset-path C:\Movie\Deepstack\datasets\vehicles
Using torch 1.7.1+cu110 CUDA:0 (NVIDIA GeForce GTX 1650 Ti, 4095MB)
Namespace(model='yolov5m', classes='', dataset_path='C:\\Movie\\Deepstack\\datasets\\vehicles', hyp='data/hyp.scratch.yaml', epochs=300, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, log_imgs=16, workers=8, project='train-runs/vehicles', name='exp', exist_ok=False, cfg='.\\models\\yolov5m.yaml', weights='yolov5m.pt', data={'train': 'C:\\Movie\\Deepstack\\datasets\\vehicles\\train', 'val': 'C:\\Movie\\Deepstack\\datasets\\vehicles\\test', 'nc': 5, 'names': ['vehicle/car', 'vehicle/truck', 'vehicle/bus', 'vehicle/train', '']}, total_batch_size=16, world_size=1, global_rank=-1, save_dir='train-runs\\vehicles\\exp6')
Start Tensorboard with "tensorboard --logdir train-runs/vehicles", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, '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}
Overriding model.yaml nc=80 with nc=5
from n params module arguments
0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 1 67680 models.common.BottleneckCSP [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 1 639168 models.common.BottleneckCSP [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 1 2550144 models.common.BottleneckCSP [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 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 1219968 models.common.BottleneckCSP [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 305856 models.common.BottleneckCSP [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 1072512 models.common.BottleneckCSP [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
24 [17, 20, 23] 1 40410 models.yolo.Detect [5, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Model Summary: 391 layers, 21501978 parameters, 21501978 gradients, 51.4 GFLOPS
Transferred 506/514 items from yolov5m.pt
Optimizer groups: 86 .bias, 94 conv.weight, 83 other
Scanning 'C:\Movie\Deepstack\datasets\vehicles\train.cache' for images and labels... 286 found, 1 missing, 0 empty, 0 corrupted: 100%|████████████| 287/287 [00:00<?, ?it/s]
Scanning 'C:\Movie\Deepstack\datasets\vehicles\test.cache' for images and labels... 64 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████| 64/64 [00:00<?, ?it/s]
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
Analyzing anchors... anchors/target = 4.80, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 8 dataloader workers
Logging results to train-runs\vehicles\exp6
Starting training for 300 epochs...
Epoch gpu_mem box obj cls total targets img_size
0%| | 0/18 [00:00<?, ?it/s]Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
NumExpr defaulting to 8 threads.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
Any ideas?
My dataset works on Google Colab..
Thanks,
Alex
Link to docs
Models do not seem to work if I also load the latest version of Yolo v6
This is a simple example that shows the issue. If I load Yolo v6 from the hub and then also try to use a model trained with this, it seems like it causes some issue. Not sure if this is a Yolo issue or an issue with this trainer. It's almost like if you load the yolo model it tries to also use the hub loaded yolo model insted of one that works with deepstack.
https://drive.google.com/file/d/1_XZErCZHSudFdDWKszpmQ8jkjKOH1kK9/view?usp=sharing
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