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@@ -96,7 +96,7 @@ function Bird_CNN() {
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<div className='request-box'>
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<div className='request-box'>
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<div ref={scrollRefUploadButton} className='input-box'>
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<div ref={scrollRefUploadButton} className='input-box'>
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<div className='button-div'>
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<div className='button-div'>
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<input disabled={loading} ref={fileInputRef} type="file" id='fileUpload' accept="image/jpeg" onChange={handleImage} style={{ display: "none" }} />
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<input disabled={loading} ref={fileInputRef} type="file" id='fileUpload' accept="image" onChange={handleImage} style={{ display: "none" }} />
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<label htmlFor="fileUpload" className="custom-button">
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<label htmlFor="fileUpload" className="custom-button">
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Select Image
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Select Image
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</label>
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</label>
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@@ -140,9 +140,7 @@ function Bird_CNN() {
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<div className='explanation-box right'>
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<div className='explanation-box right'>
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<h3>Model Architecture</h3>
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<h3>Model Architecture</h3>
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<div style={{ display: "inline" }}>
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<div style={{ display: "inline" }}>
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<span>The model used for classification is a convolutional neural network (CNN) based on depthwise separable convolutions, as introduced in the </span>
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<span>The model used for classification is a convolutional neural network. The model uses who knows how many layers, a dropout and multiple batchnormalsation.</span>
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<a href='https://arxiv.org/pdf/1610.02357' target='_blank' rel='noopener noreferrer'>Xception: Deep Learning with Depthwise Separable Convolutions</a>
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<span> paper by François Chollet. The model uses who knows how many layers, a dropout and multiple batchnormalsation.</span>
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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@@ -1,7 +1,7 @@
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function Homepage() {
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function Homepage() {
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return (
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return (
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<>
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<>
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<h1 style={{ margin: "120px 40px 40px 40px" }}>Ignore this page</h1>
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<h1 style={{ margin: "120px 40px 40px 40px" }}>Work in Progress, ignore this page and visit other Pages</h1>
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</>
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</>
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);
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);
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}
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}
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@@ -20,6 +20,8 @@
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display: flex;
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display: flex;
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align-items: center;
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align-items: center;
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justify-content: center;
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justify-content: center;
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font-size: 1.1rem;
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font-weight: bold;
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}
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}
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.navbar-item:hover {
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.navbar-item:hover {
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