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Castor Odintsov
Castor Odintsov

Download IOU L2 Image from Cisco and Run it on GNS3 - Step by Step Tutorial



The files required for the IOU are as follows. You can update the files to be downloaded by pressing the Refresh button. Select the existing Cisco IOU L2 15.2.d file (i86bi-linux-l2-adventerprisek9-15.2d.bin) and check if the GNS3 VM Server is running before clicking Next.




download iou l2 image



Could someone lead me in the right direction for getting IOS/IOU Images for GNS3?From what I've been reading, I may need to get a service contract with cisco to even get them.What would be the best way to get these images?


If you are a gns3 user desirous of running complex labs, one of the devices you would love to have is the Cisco IOU L2 that allows you simulate the Cisco switch on gns3. While it is relatively easy to download the image and import it to gns3, bringing it up is not quite easy as you will be required to generate its license key before it can work.


In this post, I will share four easy steps to generate and install the Cisco IOU L2 keygen license for use in gns3. I am assuming that your gns3 is installed and linked to the gns3 VMware and that your Cisco IOU L2 image has been download and successfully imported into the Cisco IOU L2 appliance.


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This example uses 3-D simulation data generated by Driving Scenario Designer and the Unreal Engine. For an example showing how to generate such simulation data, see Depth and Semantic Segmentation Visualization Using Unreal Engine Simulation (Automated Driving Toolbox). The 3-D simulation environment generates the images and the corresponding ground truth pixel labels. Using the simulation data avoids the annotation process, which is both tedious and requires a large amount of human effort. However, domain shift models trained on only simulation data do not perform well on real-world data sets. To address this, you can use domain adaptation to fine-tune the trained model to work on a real-world data set.


Download the simulation and real data sets by using the downloadDataset function, defined in the Supporting Functions section of this example. The downloadDataset function downloads the entire CamVid data set and partition the data into training and test sets.


The simulation data set was generated by Driving Scenario Designer. The generated scenarios, which consist of 553 photorealistic images with labels, were rendered by the Unreal Engine. You use this data set to train the model.


The real data set is a subset of the CamVid data set from the University of Cambridge. To adapt the model to real-world data, 69 CamVid images. To evaluate the trained model, you use 368 CamVid images.


The downloaded files include the pixel labels for the real domain, but note that you do not use these pixel labels in the training process. This example uses the real domain pixel labels only to calculate the mean intersection over union (IoU) value to evaluate the efficacy of the trained model.


The helper function downloadDataset downloads both the simulation and real data sets from the specified URLs to the specified folder locations if they do not exist. The function returns the paths of the simulation, real training data, and real testing data. The function downloads the entire CamVid data set and partition the data into training and test sets using the subsetCamVidDatasetFileNames mat file, attached to the example as a supporting file.


Download a pretrained network by using the helper function downloadPretrainedYOLOv3Detector to avoid having to wait for training to complete. If you want to train the network with a new set of data, set the doTraining variable to true.


This example uses a small labeled data set that contains 295 images. Many of these images come from the Caltech Cars 1999 and 2001 data sets, created by Pietro Perona and used with permission. Each image contains one or two labeled instances of a vehicle. A small data set is useful for exploring the YOLO v3 training procedure, but in practice, more labeled images are needed to train a robust network.


Note: In case of multiple classes, the data can also be organized as three columns where the first column contains the image file names with paths, the second column contains the bounding boxes and the third column must be a cell vector that contains the label names corresponding to each bounding box. For more information on how to arrange the bounding boxes and labels, see boxLabelDatastore.


The values of the bounding boxes should be finite, positive, non-fractional, non-NaN and should be within the image boundary with a positive height and width. Any invalid samples must either be discarded or fixed for proper training.


Specify the network input size. When choosing the network input size, consider the minimum size required to run the network itself, the size of the training images, and the computational cost incurred by processing data at the selected size. When feasible, choose a network input size that is close to the size of the training image and larger than the input size required for the network. To reduce the computational cost of running the example, specify a network input size of [227 227 3].


First, use transform to preprocess the training data for computing the anchor boxes, as the training images used in this example are bigger than 227-by-227 and vary in size. Specify the number of anchors as 6 to achieve a good tradeoff between number of anchors and mean IoU. Use the estimateAnchorBoxes function to estimate the anchor boxes. For details on estimating anchor boxes, see Estimate Anchor Boxes From Training Data. In case of using a pretrained YOLOv3 object detector, the anchor boxes calculated on that particular training dataset need to be specified. Note that the estimation process is not deterministic. To prevent the estimated anchor boxes from changing while tuning other hyperparameters, set the random seed prior to estimation using rng.


Use the minibatchqueue function to split the preprocessed training data into batches with the supporting function createBatchData which returns the batched images and bounding boxes combined with the respective class IDs. For faster extraction of the batch data for training, dispatchInBackground should be set to "true" which ensures the usage of parallel pool.


The YOLOv3 dataloader assumes the training/validation split is already done and data is prepared inKITTI format: images and labels are in two separate folders and each image file has a correspondingtxt label file in the label folder with the same filename. The label file content should also followKITTI format.


Option 1: Using the training data loader to load the training images for INT8 calibration.This option is now the recommended approach to support multiple image directories by leveragingthe training dataset loader. This also ensures two important aspects of data during calibration:


Option 2: Pointing the tool to a directory of images that you want to use to calibratethe model. For this option, make sure to create a sub-sampled directory of random images thatbest represent your training dataset.


--cal_image_dir parameter for images and applies the necessary preprocessingto generate a tensorfile at the path mentioned in the --cal_data_fileparameter, which is in turn used for calibration. The number of batches in thetensorfile generated is obtained from the value set to the --batches parameter,and the batch_size is obtained from the value set to the --batch_sizeparameter. Be sure that the directory mentioned in --cal_image_dir has at leastbatch_size * batches number of images in it. The valid image extensions are .jpg,.jpeg, and .png. In this case, the input_dimensions of the calibration tensorsare derived from the input layer of the .tlt model.


Block diagram of proposed model for disease image segmentation. The orange block indicates the improved backbone, the blue block indicates operations of convolution, pooling, concatenation and upsampling


Block diagram of the improved backbone network for disease image segmentation a represents the improved backbone blocks and b shows the three branches of hybrid attention


where frem represents the frequency of occurrences of pixels of class m divided by the total number of pixels in any image containing this class, and median_fre represents the median of these frequencies for all the classes.


YOLOv4-tiny supports two data formats: the sequence format (images folder and raw labels folderwith KITTI format) and the tfrecords format (images folder and TFRecords). From our experience, if mosaicaugmentation is disabled (mosaic_prob=0), training with TFRecords format is faster. If mosaicaugmentation is enabled (mosaic_prob>0), training with sequence format is faster. Thetrain and evaluate command will determine the data format based on yourdataset_config.


The YOLOv4-tiny dataloader assumes the training/validation split is already done and the data isprepared in KITTI format: images and labels are in two separate folders, where each image in theimage folder has a .txt label file with the same filename in the label folder, and thelabel file content follows KITTI format. The COCO data format is supported but only through TFRecords.Prepare the TFRecords using dataset_convert.


The following is an example dataset_config element if you want to use tfrecords formatthat was converted from KITTI data format. Here, we assume your tfrecords are all generated under a folder called tfrecords, which is undersame parent folder with images and labels:


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