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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+confusion_matrix.png filter=lfs diff=lfs merge=lfs -text
+results.png filter=lfs diff=lfs merge=lfs -text

OceanCV_FirstPass.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a47ac989ba0aace9b298a08317846eefc6810544527fdd4b01554a92a9dbf1ee
+size 118404830

README.md

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+---
+language: en
+license: mit
+tags:
+- ocean
+- computer-vision
+- yolo
+- detection
+- marine-science
+datasets:
+- OceanCV
+metrics:
+- map
+- precision
+- recall
+emoji: 🦑
+---
+
+# OceanCV_FirstPass
+
+Unified marine object detection model.
+
+### Dataset Overview
+- **Total Images**: 8,605
+- **Total Annotations**: 85,557
+- **Classes**: 1 (object)
+- **Input Resolution**: 1024x1024
+
+### Performance Metrics (Epoch 87)
+| Metric | Value |
+| :--- | :--- |
+| **mAP50** | 85.1% |
+| **mAP50-95** | 61.3% |
+| **Precision** | 87.7% |
+| **Recall** | 77.8% |
+
+### Visualizations
+![Results](results.png)
+![Confusion Matrix](confusion_matrix.png)
+![Labels](labels.jpg)
+
+### Usage
+```python
+from ultralytics import YOLO
+model = YOLO("OceanCV_FirstPass.pt")
+results = model.predict("image.jpg", imgsz=1024)
+```

app.py

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+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2
+
+st.set_page_config(page_title="OceanCV Object Detection", page_icon="🦑")
+
+st.title("OceanCV FirstPass Detector")
+st.write("Upload marine imagery to detect underwater objects.")
+
+@st.cache_resource
+def load_model():
+    return YOLO("OceanCV_FirstPass.pt")
+
+model = load_model()
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+conf = st.slider("Confidence Threshold", 0.1, 1.0, 0.5)
+iou = st.slider("IOU Threshold", 0.1, 1.0, 0.45)
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    if st.button("Detect Objects"):
+        with st.spinner("Analyzing..."):
+            results = model.predict(image, conf=conf, iou=iou, imgsz=1024)
+            
+            res_plotted = results[0].plot()
+            res_rgb = cv2.cvtColor(res_plotted, cv2.COLOR_BGR2RGB)
+            
+            st.image(res_rgb, caption="Processed Image", use_column_width=True)
+            
+            st.write(f"Detected {len(results[0].boxes)} objects.")

requirements.txt

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+ultralytics
+streamlit
+pillow
+numpy
+opencv-python-headless