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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-01-17 17:52:28 -08:00
parent cdb4680390
commit dec2c7d9a6
5 changed files with 64 additions and 32 deletions
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9
test.py
View File

@ -17,7 +17,8 @@ def test(cfg,
iou_thres=0.5, # for nms iou_thres=0.5, # for nms
save_json=False, save_json=False,
model=None, model=None,
dataloader=None): dataloader=None,
single_cls=False):
# Initialize/load model and set device # Initialize/load model and set device
if model is None: if model is None:
device = torch_utils.select_device(opt.device, batch_size=batch_size) device = torch_utils.select_device(opt.device, batch_size=batch_size)
@ -45,7 +46,7 @@ def test(cfg,
# Configure run # Configure run
data = parse_data_cfg(data) data = parse_data_cfg(data)
nc = int(data['classes']) # number of classes nc = 1 if single_cls else int(data['classes']) # number of classes
path = data['valid'] # path to test images path = data['valid'] # path to test images
names = load_classes(data['names']) # class names names = load_classes(data['names']) # class names
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
@ -216,6 +217,7 @@ if __name__ == '__main__':
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'") parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu') parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
opt = parser.parse_args() opt = parser.parse_args()
opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
print(opt) print(opt)
@ -229,7 +231,8 @@ if __name__ == '__main__':
opt.img_size, opt.img_size,
opt.conf_thres, opt.conf_thres,
opt.iou_thres, opt.iou_thres,
opt.save_json) opt.save_json,
opt.single_cls)
elif opt.task == 'benchmark': elif opt.task == 'benchmark':
# mAPs at 320-608 at conf 0.5 and 0.7 # mAPs at 320-608 at conf 0.5 and 0.7

44
train.py
View File

@ -74,7 +74,7 @@ def train():
data_dict = parse_data_cfg(data) data_dict = parse_data_cfg(data)
train_path = data_dict['train'] train_path = data_dict['train']
test_path = data_dict['valid'] test_path = data_dict['valid']
nc = int(data_dict['classes']) # number of classes nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
# Remove previous results # Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file): for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
@ -177,7 +177,8 @@ def train():
hyp=hyp, # augmentation hyperparameters hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training rect=opt.rect, # rectangular training
cache_labels=True, cache_labels=True,
cache_images=opt.cache_images) cache_images=opt.cache_images,
single_cls=opt.single_cls)
# Dataloader # Dataloader
batch_size = min(batch_size, len(dataset)) batch_size = min(batch_size, len(dataset))
@ -194,7 +195,8 @@ def train():
hyp=hyp, hyp=hyp,
rect=True, rect=True,
cache_labels=True, cache_labels=True,
cache_images=opt.cache_images), cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size * 2, batch_size=batch_size * 2,
num_workers=nw, num_workers=nw,
pin_memory=True, pin_memory=True,
@ -202,6 +204,7 @@ def train():
# Start training # Start training
nb = len(dataloader) nb = len(dataloader)
prebias = start_epoch == 0
model.nc = nc # attach number of classes to model model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
@ -213,24 +216,22 @@ def train():
torch_utils.model_info(model, report='summary') # 'full' or 'summary' torch_utils.model_info(model, report='summary') # 'full' or 'summary'
print('Using %g dataloader workers' % nw) print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs) print('Starting training for %g epochs...' % epochs)
for epoch in range(start_epoch - 1 if opt.prebias else start_epoch, epochs): # epoch ------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------
model.train() model.train()
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
# Prebias # Prebias
if opt.prebias: if prebias:
if epoch < 0: # prebias if epoch < 20: # prebias
ps = 0.1, 0.9, False # prebias settings (lr=0.1, momentum=0.9, requires_grad=False) ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9)
else: # normal training else: # normal training
ps = hyp['lr0'], hyp['momentum'], True # normal training settings ps = hyp['lr0'], hyp['momentum'] # normal training settings
opt.prebias = False print_model_biases(model)
prebias = False
for p in optimizer.param_groups: # Bias optimizer settings
p['lr'] = ps[0] # learning rate optimizer.param_groups[2]['lr'] = ps[0]
if p.get('momentum') is not None: # for SGD but not Adam if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
p['momentum'] = ps[1] optimizer.param_groups[2]['momentum'] = ps[1]
for name, p in model.named_parameters():
p.requires_grad = True if name.endswith('.bias') else ps[2]
# Update image weights (optional) # Update image weights (optional)
if dataset.image_weights: if dataset.image_weights:
@ -239,6 +240,7 @@ def train():
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses mloss = torch.zeros(4).to(device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start) ni = i + nb * epoch # number integrated batches (since train start)
@ -307,10 +309,7 @@ def train():
# Process epoch results # Process epoch results
final_epoch = epoch + 1 == epochs final_epoch = epoch + 1 == epochs
if opt.prebias: if not opt.notest or final_epoch: # Calculate mAP
print_model_biases(model)
continue
elif not opt.notest or final_epoch: # Calculate mAP
is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80 is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
results, maps = test.test(cfg, results, maps = test.test(cfg,
data, data,
@ -320,7 +319,8 @@ def train():
conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed
iou_thres=0.6 if final_epoch and is_coco else 0.5, iou_thres=0.6 if final_epoch and is_coco else 0.5,
save_json=final_epoch and is_coco, save_json=final_epoch and is_coco,
dataloader=testloader) dataloader=testloader,
single_cls=opt.single_cls)
# Update scheduler # Update scheduler
scheduler.step() scheduler.step()
@ -412,10 +412,10 @@ if __name__ == '__main__':
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='initial weights') parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='initial weights')
parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # defaultpw, uCE, uBCE parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # defaultpw, uCE, uBCE
parser.add_argument('--prebias', action='store_true', help='pretrain model biases')
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)') parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--adam', action='store_true', help='use adam optimizer') parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--var', type=float, help='debug variable') parser.add_argument('--var', type=float, help='debug variable')
opt = parser.parse_args() opt = parser.parse_args()
opt.weights = last if opt.resume else opt.weights opt.weights = last if opt.resume else opt.weights

5
utils/datasets.py
View File

@ -263,7 +263,7 @@ class LoadStreams: # multiple IP or RTSP cameras
class LoadImagesAndLabels(Dataset): # for training/testing class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_labels=False, cache_images=False): cache_labels=False, cache_images=False, single_cls=False):
path = str(Path(path)) # os-agnostic path = str(Path(path)) # os-agnostic
assert os.path.isfile(path), 'File not found %s. See %s' % (path, help_url) assert os.path.isfile(path), 'File not found %s. See %s' % (path, help_url)
with open(path, 'r') as f: with open(path, 'r') as f:
@ -343,7 +343,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows
nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
if single_cls:
l[:, 0] = 0 # force dataset into single-class mode
self.labels[i] = l self.labels[i] = l
nf += 1 # file found nf += 1 # file found

4
utils/evolve.sh
View File

@ -8,8 +8,8 @@
#t=ultralytics/yolov3:v199 && sudo docker pull $t && sudo nvidia-docker run -it --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t python3 train.py --data coco2014.data --img-size 672 --epochs 10 --batch 16 --accum 4 --weights '' --arc defaultpw --device 0 --multi #t=ultralytics/yolov3:v199 && sudo docker pull $t && sudo nvidia-docker run -it --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t python3 train.py --data coco2014.data --img-size 672 --epochs 10 --batch 16 --accum 4 --weights '' --arc defaultpw --device 0 --multi
while true; do while true; do
python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --evolve --device $1 --cfg yolov3-tiny-3cls.cfg --cache # python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --pre --multi --bucket ult/wer --evolve --device $1 --cfg yolov3-tiny-3cls.cfg --cache
# python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg
# python3 train.py --data coco2014.data --img-size 640 --epochs 10 --batch 22 --accum 3 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/640ms_coco2014_10e --device $1 --multi # python3 train.py --data coco2014.data --img-size 640 --epochs 10 --batch 22 --accum 3 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/640ms_coco2014_10e --device $1 --multi
# python3 train.py --data coco2014.data --img-size 320 --epochs 27 --batch 64 --accum 1 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/320_coco2014_27e --device $1 # python3 train.py --data coco2014.data --img-size 320 --epochs 27 --batch 64 --accum 1 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/320_coco2014_27e --device $1

34
utils/gcp.sh
View File

@ -38,11 +38,17 @@ python3 detect.py
# Test # Test
python3 test.py --save-json python3 test.py --save-json
# Kill All
t=ultralytics/yolov3:v206
docker kill $(docker ps -a -q --filter ancestor=$t)
t=ultralytics/yolov3:v208
docker kill $(docker ps -a -q --filter ancestor=$t)
# Evolve # Evolve
sudo -s sudo -s
t=ultralytics/yolov3:v206 t=ultralytics/yolov3:v206
docker kill $(docker ps -a -q --filter ancestor=$t) docker kill $(docker ps -a -q --filter ancestor=$t)
for i in 6 7 for i in 4 5 6 7
do do
docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i
# docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i # docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i
@ -55,10 +61,10 @@ done
sudo -s sudo -s
t=ultralytics/yolov3:v208 t=ultralytics/yolov3:v208
docker kill $(docker ps -a -q --filter ancestor=$t) docker kill $(docker ps -a -q --filter ancestor=$t)
for i in 0 for i in 0 1
do do
# docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i # docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i
docker pull $t && docker run --gpus all -it --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i
# docker pull $t && nvidia-docker run -d -v "$(pwd)"/coco:/usr/src/coco $t bash utils/evolve.sh $i # docker pull $t && nvidia-docker run -d -v "$(pwd)"/coco:/usr/src/coco $t bash utils/evolve.sh $i
# docker pull $t && nvidia-docker run -d -v /mnt/disks/nvme0n1/coco:/usr/src/coco $t bash utils/evolve.sh $i # docker pull $t && nvidia-docker run -d -v /mnt/disks/nvme0n1/coco:/usr/src/coco $t bash utils/evolve.sh $i
sleep 180 sleep 180
@ -277,6 +283,19 @@ n=211 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gp
n=212 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg n=212 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
n=213 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg n=213 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
n=214 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg n=214 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
n=215 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
n=217 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 6 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=219 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=220 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 1 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=221 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 30 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 2 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=222 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 40 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 3 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=223 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=224 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 1 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=225 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 30 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=226 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 40 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=227 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=228 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
n=229 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
# sm4 # sm4
n=201 && t=ultralytics/yolov3:v201 && sudo docker pull $t && sudo nvidia-docker run -d -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --device 0 --cfg yolov3-tiny-3cls.cfg n=201 && t=ultralytics/yolov3:v201 && sudo docker pull $t && sudo nvidia-docker run -d -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --device 0 --cfg yolov3-tiny-3cls.cfg
@ -287,3 +306,12 @@ n=205 && t=ultralytics/yolov3:v202 && sudo docker pull $t && sudo nvidia-docker
n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --notest --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --notest --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
n=209 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 3 --cfg yolov3-tiny-3cls.cfg n=209 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 3 --cfg yolov3-tiny-3cls.cfg
n=210 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-3cls.cfg n=210 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-3cls.cfg
n=216 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
n=218 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --pre --multi --bucket ult/wer --name $n --nosave --cache --device 7 --cfg yolov3-tiny-3cls.cfg
n=230 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single
n=231 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-1cls.cfg --single
n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it --ipc=host -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 10 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it --ipc=host -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 10 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --nosave --cache --device 1 --cfg yolov3-tiny-3cls.cfg