insigh1 / interactive_abcs_with_american_sign_language_using_yolov5 Goto Github PK

Excuse me, Do you have a pycharm version of the project that can be run? I am a amateur of computer vision algorithm, and I need a executable project. If you can give me, I will be very grateful to you. My e-mail : [email protected]

Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15109MB)

Namespace(adam=False, batch_size=64, bucket='', cache_images=False, cfg='models/yolov5s.yaml', data='asl.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, logdir='runs/', multi_scale=False, name='asl_example', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=64, weights='yolov5s.pt', workers=8, world_size=1)
Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/
2021-02-19 17:18:24.635404: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
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=28

             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 89001 models.yolo.Detect [28, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 191 layers, 7.32791e+06 parameters, 7.32791e+06 gradients, 17.0 GFLOPS

Transferred 362/370 items from yolov5s.pt
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Scanning labels asl_yolo/labels/train.cache (19113 found, 0 missing, 9 empty, 0 duplicate, for 19122 images): 19122it [00:01, 15994.32it/s]
Scanning labels asl_yolo/labels/validation.cache (4779 found, 0 missing, 9 empty, 0 duplicate, for 4788 images): 4788it [00:00, 7887.93it/s]
NumExpr defaulting to 2 threads.

Analyzing anchors... anchors/target = 2.52, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 2 dataloader workers
Logging results to runs/exp18_asl_example
Starting training for 3 epochs...

 Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size

0% 0/299 [00:00<?, ?it/s]Traceback (most recent call last):
File "train.py", line 456, in
train(hyp, opt, device, tb_writer)
File "train.py", line 268, in train
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
File "/content/drive/My Drive/ASLR/yolov5/utils/general.py", line 525, in compute_loss
lbox *= h['gioU'] * s
KeyError: 'gioU'
0% 0/299 [00:02<?, ?it/s]

I want any your account to contact with you, please

Can you share the weight file that you have trained?

I came with the problem in training the model.
when running the code !python train.py --img 1024 --batch 16 --epochs 3 --data asl.yaml --cfg models/yolov5s.yaml --name asl_example , I failed with
Runtimeerror: [enforce fail at ..\caffe2\serialize\inline_container.cc:145] . PytorchStreamReader failed reading zip achive: failed finding central directory.
error is in train.py line 77 ckpt = torch.load(weights, map_location=device) #load checkpoint
I didnt see any zip file, also the document about checkpoint
Can you answer my question? plz.

Hello, I am in the direction of AI algorithm. I sincerely ask you for gesture your training data set, and I can exchange it with you. I have 40Gb gesture classification data and 5000 gesture data of target detection. I do not do business, but I study for the purpose. I hope you can share the data with me. Thank you [email protected] Or through Baidu disk or through Google and other ways.

How created ASL.yaml file In step 3?

Are there any other ways to download the dataset? It seems doesn't work on roboflow.

Thanks to the author for an excellent job. I have some questions for you,

1. How do I get 720 images and labels?
2. The 720 images and labels grew into data augmentation to 18,000 images and labels, and what scripts will need to be run?

Hi, I have a problem with Modeling step,
I can't pass more than 100 epochs, the training held in 100, I need more than 100 epochs without hold, how I can solve this problem?