sayedM commited on
Commit
8b49954
·
1 Parent(s): b8d8df9

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +38 -26
app.py CHANGED
@@ -68,38 +68,50 @@ def get_results(image, prompt):
68
 
69
  # Define the text description within an HTML <div> element
70
  description_html = """
 
71
  <html>
72
  <head>
73
- <style>
74
- .description {
75
- margin: 20px;
76
- padding: 10px;
77
- border: 1px solid #ccc;
78
- }
79
- </style>
80
  </head>
81
  <body>
82
- <div class="description">
83
- <p><strong>Description:</strong></p>
84
- <p>We present a demo for performing object segmentation with training a Yolov8-seg on wheel Image dataset. The model was trained on 696 training images and validated on 199 images.</p>
85
- <p><strong>Usage:</strong></p>
86
- <p>You can upload wheel Image images, and the demo will provide you with your segmented image.</p>
87
- <p><strong>Dataset:</strong></p>
88
- <p>This dataset comprises a total of 994 images, which are divided into three distinct sets for various purposes:</p>
89
- <ul>
90
- <li><strong>Training Set:</strong> It includes 696 images and is intended for training the model.</li>
91
- <li><strong>Validation Set:</strong> There are 199 images in the validation set, which is used for optimizing model parameters during development.</li>
92
- <li><strong>Test Set:</strong> This set consists of 99 images and serves as a separate evaluation dataset to assess the performance of trained models.</li>
93
- </ul>
94
- <p><strong>License:</strong> This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
95
- <p>To access and download this dataset, please follow this link: <a href="https://universe.roboflow.com/project-wce7s/1000_seg_wheel" target="_blank">Dataset Download</a></p>
96
- <p><strong>Download Dataset:</strong></p>
97
- <p>To download the dataset we used, you can use the following command in colab:</p>
98
- <pre>!wget https://universe.roboflow.com/ds/OPPOJjnJPs?key=5yzDMD610e</pre>
99
- <p>Feel free to explore and use this repository for your object segmentation needs. If you have any questions or need assistance, please don't hesitate to reach out.</p>
100
- </div>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
101
  </body>
102
  </html>
 
103
  """
104
  title = "autoannotation"
105
 
 
68
 
69
  # Define the text description within an HTML <div> element
70
  description_html = """
71
+ <!DOCTYPE html>
72
  <html>
73
  <head>
74
+ <title>Tuba AI Auto-Annotation </title>
 
 
 
 
 
 
75
  </head>
76
  <body>
77
+ <h1>Tuba AI Auto-Annotation Model 🚀</h1>
78
+ <h2>Saving Time,Bounding Boxes at a Time </h2>
79
+
80
+
81
+ <h2>Introduction</h2>
82
+ <p>Welcome to the world of DevisionX, where AI meets vision to revolutionize annotation. Our mission is to make computer vision accessible to all, and this README is your gateway to understanding how our auto-annotation model can change the way you work.</p>
83
+
84
+
85
+
86
+ <h2>Meet Tuba.AI - Your Partner in Vision</h2>
87
+
88
+ <h3>What is Tuba?</h3>
89
+ <p>Tuba is the secret sauce behind DevisionX, your no-code/low-code companion for all things computer vision. It's your toolkit for labeling, training data, and deploying AI-vision applications faster and easier than ever before.</p>
90
+
91
+ <ul>
92
+ <li>No-Code/Low-Code: Say goodbye to complex coding. Tuba's user-friendly interface makes it accessible to everyone.</li>
93
+ <li>Labeling Made Easy: Annotate your data effortlessly with Tuba's intuitive tools.</li>
94
+ <li>Faster Deployment: Deploy your AI models with ease, whether you're building a standalone app or integrating within an existing one.</li>
95
+ <li>State-of-the-Art Technology: Tuba is powered by the latest AI tech and follows production-ready standards.</li>
96
+ </ul>
97
+
98
+ <h2>The DevisionX Auto-Annotation</h2>
99
+ <p>Our auto-annotation model is a game-changer. It takes input text and images, weaving them together to generate precise bounding boxes. This AI marvel comes with a plethora of benefits:</p>
100
+
101
+ <ul>
102
+ <li>Time Saver: Say goodbye to hours of manual annotation. Let our model do the heavy lifting.</li>
103
+ <li>Annotation Formats: It speaks the language of YOLO and COCO, making it versatile for various projects.</li>
104
+ <li>Human Assistance: While it's incredibly efficient, it also respects human creativity and can be your reliable assistant.</li>
105
+ </ul>
106
+
107
+
108
+ <h2>Let's Build Together</h2>
109
+ <p>DevisionX and Tuba are here to redefine the way you approach computer vision. Join us in this exciting journey, where AI meets creativity, and innovation knows no bounds.</p>
110
+
111
+ <p>Get started today and be a part of the future of vision.</p>
112
  </body>
113
  </html>
114
+
115
  """
116
  title = "autoannotation"
117