Create README.md

README.md

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