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--- |
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license: apache-2.0 |
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language: |
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- ar |
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- en |
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library_name: transformers |
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pipeline_tag: object-detection |
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tags: |
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- climate |
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--- |
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# DETR-BASE_Marine |
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## Overview |
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+ Model Name: DETR-BASE_Marine |
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+ Model Architecture: DETR (End-to-End Object Detection) with ResNet-50 backbone. |
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+ Model Type: Object Detection |
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+ Framework: PyTorch |
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+ Dataset: Aerial Maritime Image Dataset |
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+ License: MIT License (for the dataset) |
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## Model Description |
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The DETR-BASE_Marine Aerial Maritime Detector is a deep learning model based on the DETR architecture with a ResNet-50 backbone. It has been fine-tuned on the "Aerial Maritime Image Dataset," which comprises 74 aerial photographs captured via a Mavic Air 2 drone. The model is designed for object detection tasks in maritime environments and can identify and locate various objects such as docks, boats, lifts, jetskis, and cars in aerial images. |
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## Key Features: |
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+ Multi-class object detection. |
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+ Object classes: Docks, Boats, Lifts, Jetskis, Cars. |
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+ Robust performance in aerial and maritime scenarios. |
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## Use Cases |
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+ **Boat Counting**: Count the number of boats on water bodies, such as lakes, using drone imagery. |
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+ **Boat Lift Detection**: Identify the presence of boat lifts on the waterfront via aerial surveillance. |
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+ **Car Detection**: Detect and locate cars within maritime regions using UAV drones. |
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+ **Habitability Assessment**: Determine the level of inhabitation around lakes and water bodies based on detected objects. |
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+ **Property Monitoring**: Identify if visitors or activities are present at lake houses or properties using drone surveillance. |
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+ **Proof of Concept**: Showcase the potential of UAV imagery for maritime projects and object detection tasks. |
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## Dataset |
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+ **Dataset Name**: Aerial Maritime Image Dataset |
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+ **Number of Images**: 74 |
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+ **Number of Bounding Boxes**: 1,151 |
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+ **Collection Method**: Captured via Mavic Air 2 drone at 400 ft altitude. |
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## Usage |
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``` python |
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from transformers import DetrImageProcessor, DetrForObjectDetection |
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import torch |
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from PIL import Image |
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img_path = "" |
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image = Image.open(img_path) |
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extractor = AutoFeatureExtractor.from_pretrained("TuningAI/DETR-BASE_Marine") |
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model = AutoModelForObjectDetection.from_pretrained("TuningAI/DETR-BASE_Marine") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# convert outputs (bounding boxes and class logits) to COCO API |
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# let's only keep detections with score > 0.9 |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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box = [round(i, 2) for i in box.tolist()] |
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print( |
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f"Detected {model.config.id2label[label.item()]} with confidence " |
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f"{round(score.item(), 3)} at location {box}" |
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) |
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``` |
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## License |
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This model is provided under the MIT License. |
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The Aerial Maritime Image Dataset used for fine-tuning is also under the MIT License. |
