If the MCL is under constant tension is can lead to increased wear and tear of the Meniscus.

Celery workers typically run the same code as the Flask app, but they not running as flask servers, so websockets from celery to flask aren't easily a thing. (I've never seen it done, but maybe someone has ironed out the tricky parts.)

Triton as an inference motor as Triton does not accept a tuple as output.

ONNX, TorchScript and CoreML format

We recommend a minimum of 300 generations of evolution for best results. Note that evolution is generally expensive and time consuming, as the base scenario is trained hundreds of times, possibly requiring hundreds or thousands of GPU hours.

Background images. Background images are images with no objects that are added to a dataset to reduce False Positives (FP). We recommend about 0-10% background images to help reduce FPs (COCO has 1000 background images for reference, 1% of the total).

(TensorFlow, TensorRT, PyTorch, ONNX Runtime, or a custom framework)

signature_def_map = { 'serving_default': tf.saved_model.predict_signature_def( {signitures['image_arrays'].name: signitures['image_arrays']}, {signitures['prediction'].name: signitures['prediction']}), }

'serving_default': tf.saved_model.predict_signature_def( {'input': inputs}, output_dict, ) }

Moreover, it is commonly best practice to label the occluded object as if it were fully visible – rather than drawing a bounding box for only the partially visible portion of the object.

groudtruth boxes values

So if your batch size is 8 the effective learning rate is 8 times lower than you specified.

add learning_rate=0.001,lr_warmup_init=0.0001 to the --hparams

Dumping it into multiple tfrecords to allow better mixing of training data got me to an AP of 0.11 after 2 epochs.

gpu nvidia 1080. I switched to a new machine using Nvidia RTX 3090 with CUDA 11.1.

properly shuffled during creation of tfRecord.

few of my bounding boxes had zero area

a trustable solution as my model never converge on training and I just gave up on using this model because the TF OD Api (using faster_rcnn_resnet101) was enough for me.

autoaugmentation_policy

export GIT_LFS_SKIP_SMUDGE=1

(a) PASCAL 2012

Figure 4

Figure 2

Figure 2

Figure 1

In this thesis, I propose three possible strategies, incre-mental relabeling, importance-weighted label prediction and active Bayesian Networks.

Use your “gold standard” data to measure the performance ofeach contributor so you know when to retrain workers. Whena contributor’s score falls below 70% accuracy, exclude hiswork and retrain.

“Gold Standard” Data: A Best Practice Methodfor Assessing Labels

If you want to design your own fancy AI pipeline, LOST will provide all the building blocks you need.

A box is considered too loose when there is too much distance between the object and the edges of the bounding box, which leads to unnecessary parts of the image background showing through within the box.

The left-hand side is rejected due to too loose bounding box

We construct a graph from the unlabeled data to representthe underlying structure, such that each node represents adata point, and edges represent the inter-relationships be-tween them. Thereafter, considering the flow of beliefs in thisgraph, we choose those samples for labeling which minimizethe joint entropy of the nodes of the graph.

Traditional Optical Character Recognition (OCR) systems