added face detection introduction text
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@@ -180,6 +180,14 @@ function Object_Detection() {
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return (
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<div className='site-box'>
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<h1 className='site-headline'>Face Detection</h1>
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<div id={styles["text-container-introduction"]}>
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<p>
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The model’s performance has been limited to 2 FPS due to low-end server hardware constraints.
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</p>
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<p>
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The webcam video is transmitted to a server for processing. The server does not store, train on, or use the data for any purpose other than face detection.
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</p>
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</div>
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<div className={styles["button-div"]}>
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<button
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id="button-send"
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@@ -200,7 +208,7 @@ function Object_Detection() {
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<canvas ref={canvasRefSending} style={{ width: "100%", height: "100%", display: "none" }} />
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</div>
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<div id={styles["text-container"]}>
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<div id={styles["text-container-explanation"]}>
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<p>
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This Project is an implementation of the YOLO (You Only Look Once) algorithm. It was implemented with Pytorch and trained on a Dataset from Roboflow.
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</p>
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@@ -13,10 +13,24 @@
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margin: 50px;
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}
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#text-container {
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#text-container-explanation {
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margin: 70px 0 0 0;
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width: 80vw;
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height: 70vh;
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height: 300px;
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align-self: center;
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position: relative;
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}
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#text-container-introduction {
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margin: 50px 0 20px 0;
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width: 70vw;
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height: fit-content;
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align-self: center;
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position: relative;
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left: -10vw;
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}
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#text-container-introduction p {
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margin: 40px 0 40px 0;
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font-size: 1.1rem;
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}
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@@ -35,7 +35,7 @@ h4 {
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.site-headline {
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margin: 50px min(100px, 10vw) 50px min(100px, 10vw);
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color: rgb(47, 168, 208);
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color: var(--surface);
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font-weight: bold;
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font-size: clamp(1rem, min(8vw, 8vh), 4rem);
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position: relative;
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@@ -50,7 +50,7 @@ h4 {
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height: 4px;
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width: 100%;
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border-radius: 20px;
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background: rgb(47, 168, 208);
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background: var(--surface);
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}
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.site-box {
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