ocleft.blogg.se - Educational anaconda lyrics

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Provided to YouTube by Universal Music Group Anaconda To start training on MNIST for example use -data mnist.CollegeHumor hello o_O 1 viewer 23.5K views 12 Contributors Anaconda (The Educational Version) Lyrics The anaconda grow, the anaconda grow The anaconda grow up to a quarter ton, hun. YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the -data argument. Reproduce by python export.py -weights yolov5s-cls.pt -include engine onnx -imgsz 224 Reproduce by python classify/val.py -data.

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Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.

Accuracy values are for single-model single-scale on ImageNet-1k dataset.

All checkpoints are trained to 90 epochs with SGD optimizer with lr0=0.001 and weight_decay=5e-5 at image size 224 and all default settings.

We ran all speed tests on Google Colab Pro for easy reproducibility. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare.

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YOLOv5 release v6.2 brings support for classification model training, validation and deployment! See full details in our Release Notes and visit our YOLOv5 Classification Colab Notebook for quickstart tutorials.

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YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with -data coco128-seg.yaml argument and manual download of COCO-segments dataset with bash data/scripts/get_coco.sh -train -val -segments and then python train.py -data coco.yaml. Reproduce by python export.py -weights yolov5s-seg.pt -include engine -device 0 -half

Export to ONNX at FP32 and TensorRT at FP16 done with export.py.

Reproduce by python segment/val.py -data coco.yaml -weights yolov5s-seg.pt -batch 1 Values indicate inference speed only (NMS adds about 1ms per image).

Speed averaged over 100 inference images using a Colab Pro A100 High-RAM instance.

Reproduce by python segment/val.py -data coco.yaml -weights yolov5s-seg.pt

Accuracy values are for single-model single-scale on COCO dataset.

All checkpoints are trained to 300 epochs with SGD optimizer with lr0=0.01 and weight_decay=5e-5 at image size 640 and all default settings.

We ran all speed tests on Google Colab Pro notebooks for easy reproducibility. We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. Reproduce by python val.py -data coco.yaml -img 1536 -iou 0.7 -augment

TTA Test Time Augmentation includes reflection and scale augmentations.

Reproduce by python val.py -data coco.yaml -img 640 -task speed -batch 1 Speed averaged over COCO val images using a AWS p3.2xlarge instance.Reproduce by python val.py -data coco.yaml -img 640 -conf 0.001 -iou 0.65 mAP val values are for single-model single-scale on COCO val2017 dataset.Nano and Small models use hyps, all others use. All checkpoints are trained to 300 epochs with default settings.Reproduce by python val.py -task study -data coco.yaml -iou 0.7 -weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5圆.pt.EfficientDet data from google/automl at batch size 8.GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.COCO AP val denotes metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.

YOLOv5 has been designed to be super easy to get started and simple to learn.