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Object Detection-Image (76 results) showing 1 - 20
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ByAMD Xilinx | Ver 2022.1 Vitis™ AI is a comprehensive acceleration platform for machine learning inference development on AMD Xilinx platforms. It consists of a series of optimized IP, software tools, libraries, deep learning models from multiple industry-standard frameworks and sample designs, with which developers can... | |
ByYobitel | Ver 1.1.1
Starting from $0.03 to $0.03/hr for software + AWS usage fees Pre-build Label Studio provides a variety of built-in labeling templates as building blocks, you don't have to create data annotation workflows from scratch. It helps export labels supporting popular ML framework formats like TensorFlow, PyTorch, sci-kit-learn, SageMaker, and others. This also creat... | |
ByNavInfo Europe B.V. | Ver 4.0.0
NavInfo Europe's face and license plate anonymizer detects and blurs recognisable faces and license plates in images. The blurring of faces and license plates helps to reach global privacy standards. The models are trained using images taken from a dashcam. For any specific solution, contact us to... | |
ByRocketML | Ver 0.1 Fast offline object detection using SSD or Yolo on batches of images for offline processing | |
ByRocketML | Ver 0.1 This model finds the vehicles and license plates; reports general vehicle attributes, for example, vehicle type (car/van/bus/track) and color; reports a string per recognized license plate. | |
BySensifai Sensifai offers automatic image recognition. This image AI system recognizes thousands of objects, attributes, and scenes from any given image. | |
ByRocketML | Ver 0.1 Text region detector automatically detects the presence of text in natural scene images. Detecting text in constrained, controlled environments can typically be accomplished by using heuristic-based approaches. Natural scene text detection is different — and much more challenging. This model... | |
Byimgproxy | Ver v3.27.2 imgproxy is a fast and secure standalone server for resizing, processing, and converting images. The guiding principles behind imgproxy are security, speed, and simplicity. imgproxy is able to quickly and easily resize images on the fly, and it's well-equipped to handle a large amount of image resi... | |
ByAmazon Web Services | Ver GPU This is an Object Detection model from [PyTorch Hub](https://pytorch.org/hub/nvidia_deeplearningexamples_ssd/). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task is to... | |
ByAmazon Web Services | Ver 1.1 Given an input image, this will return object coordinates and category predictions. The format of coordinates is encoded as (left, top, right, bottom) of the absolute pixel locations. This model is trained on COCO dataset with 80 common object categories. It can be used as fast and reliable general... | |
IBM Maximo Visual Inspection puts the power of computer vision AI capabilities into the hands of your quality control and inspection teams. It makes computer vision, deep learning and automation more accessible to your inspectors and technicians by providing an intuitive toolset for labeling,... | |
ByInmatura | Ver 1.0.0 This model provides object detection on images using a Mask R-CNN (ResNetXt 101 + FPN) architecture. This network provides state of the art accuracy the COCO2017 validation (Box AP: 43.0) and at the same time it provides fast inference times. Supports both CPU and GPU and it features a simple... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/efficientdet/d4/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task is to identify... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_1024x1024/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the... | |
ByAmazon Web Services | Ver 1.1 Given an input image, this model will return object coordinates and category predictions. The format of coordinates is encoded as (left, top, right, bottom) of the absolute pixel locations. This model is trained on COCO dataset with 80 common object categories. It can be used as fast and reliable... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_1024x1024/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the... | |
ByAmazon Web Services | Ver GPU This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_640x640/1). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the... |