Custom Object Detection
This is a custom single object detection model used to detect a specific object in a given image.
Object Detection
Objection detection is one of the key use cases for CV. The job requires to detect the objects and coordinates in a given image. In this image, we. are showing the examples for single object detection. The same can be expanded to multiple objects.
Input
Prompt* String
*
number
(minimum: 0, maximum: 1)
Default: 0.7
*
number
(minimum: 0, maximum: 1)
Default: 0.7
*
integer
(maximum: 1)
Default: 0.7
Input:
Output
Model output for Object Detection (Example 1)
DL Backtrace for example 1
GradCAM for example 1
Model Description:
Model Architecture for SingleObject Detection:
- Input:Accepts an image of shape (224, 224, 3).
- Convolutional Blocks:
- 5 sequential blocks of Conv2D layers withReLUactivation and MaxPooling2D for feature extraction.
- Filters progress as 32 → 64 → 128 → 256 → 512.
- Global Pooling:
- GlobalAveragePooling2D reduces spatialdimensions to a single vector.
- Dense Layers:
- Two fully connected layers with 512 and256 units for feature refinement.
- Output Layer:
- Dense(4, activation='sigmoid') outputs 4normalized values representing bounding box coordinates: [x_min,y_min,x_max,y_max].
Text Summarization using T5
Text summarization using the T5 (Text-to-Text Transfer Transformer) small model is a natural language processing (NLP) task where the model generates concise summaries of input text. T5 is a transformer-based model developed by Google that treats every NLP problem as a text-to-text task.
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