Freezing layer 'model.23.dfl.conv.weight' #156 New issue Closed xgli411 opened this issue May 31, 2024· 2 comments Comments xgli411 commented May 31, 2024 When I train with yolov10n.yaml, why does it freeze by default? How do I lift the freeze? Thank you very much for your ...
Freezing layer 'model.30.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n... AMP: checks passed ✅ train: Scanning /home/azureuser/data/datadisk/trainingdataset/labels/val.cache... WARNING⚠️No 'flip_idx' array defined in data.yaml, setting augmentatio...
Is the structure diagram of the LiteDetect lightweight detection head. Feature Map refers to the input feature map from the upper layer; ConvBlock is a custom convolution module; CV2 is the bounding box prediction module; CV3 is the category prediction module; DFL (Distribution Focal Loss) is...
The recall value of 0.9733 for DFL-ConvGRU also demonstrates its ability to capture a large proportion of true positive theft instances. Additionally, the F1 score of DFL-ConvGRU is 0.9758, which is consistently higher than the F1 scores of the other models. The F1 score considers both ...
The values of the remaining parameters are as follows: weight_decay = 0.00053, warmup_epochs = 2.82085, box = 6.81459, cls = 0.53361, dfl = 1.00486, epoch = 50, batch_size = 16. The input image size is (640,640), where lr0 is set at a higher value to accelerate the initial ...
STATh Double Value from which the weight is calculated if the observation is head STATdd Double Value from which the weight is calculated if the observation is the temporal change in head STATFLAG Short Integer STATFLAG coded value domain PLOTSYMBOL Long Integer An integer intended to contro...
To this end, the proposed method adopts the feature extraction network similar to the lightweight model CSPDarknet, consisting of CBS, C2f, and Spatial Pyramid Pooling Fast (SPPF) modules, as listed in Table 3. The CBS module contains Convolution (Conv), Batch Normalization (BN), and ...
auto forward_feat_cv2_x2_0 = NAMESPACE_YOLOv10::convBlock(network, weightMap, *c2fcib_22->getOutput(0), c2, 3, 1, 1, "model.23.one2one_cv2.2.0"); auto forward_feat_cv2_x2_1 = NAMESPACE_YOLOv10::convBlock(network, weightMap, *forward_feat_cv2_x2_0->getOutput(0), c2...
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, fl_gamma=0.0, label_smoothing=0.0, 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...
[1] 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] 22 [15, 18, 21] 1 3800014 ultralytics.nn.modules.head.Detect [42, [192, 384, 576]] YOLOv8m summary: 295 layers, 25880638 parameters, 25880622 gradients Freezing layer 'model.22.dfl.conv.weight' AMP: running ...