Object detection - Image class
fouxa
2.7 ★
store rating
1,000+
downloads
Free
With this image class app, you can detect various objects in images using different models like YOLO, PoseNet, and MobileNet. Includes offline functionality, multiple detection models, and pose estimation features.
AppRecs review analysis
AppRecs rating 2.7. Trustworthiness 0 out of 100. Review manipulation risk 0 out of 100. Based on a review sample analyzed.
★★☆☆☆
2.7
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4 star
14%
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2 star
14%
1 star
43%
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Mixed user feedback
Average 2.7★ rating suggests room for improvement
About Object detection - Image class
In this app, you can detect various objects using the best object detection models.
🏮These object detection models are modified to work on mobile platforms.
🏮You can use different types of models like:
🔦 YOLO
-It is fast and can detect various objects like cars, utensils, electronic gadgets, furniture, humans and so on...
🔦 PoseNet (by TensorFlow)
-It allows you to estimate and track human poses (known as “pose estimation”) by detecting body parts such as elbows, hips, wrists, knees, and ankles.
🔦 MobileNet
-MobileNets are small, low-latency, low-power models used to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings, and segmentation similar to how other popular large-scale models, such as Inception, are used.
🔦 MobileNet SSD
The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework.
🏮All models can work without internet
🏮These object detection models are modified to work on mobile platforms.
🏮You can use different types of models like:
🔦 YOLO
-It is fast and can detect various objects like cars, utensils, electronic gadgets, furniture, humans and so on...
🔦 PoseNet (by TensorFlow)
-It allows you to estimate and track human poses (known as “pose estimation”) by detecting body parts such as elbows, hips, wrists, knees, and ankles.
🔦 MobileNet
-MobileNets are small, low-latency, low-power models used to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings, and segmentation similar to how other popular large-scale models, such as Inception, are used.
🔦 MobileNet SSD
The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework.
🏮All models can work without internet
