From 77a384f64700b9e1e800cfcc0a2bd72babac1231 Mon Sep 17 00:00:00 2001
From: LJ5O <75009579+LJ5O@users.noreply.github.com>
Date: Mon, 28 Apr 2025 16:55:12 +0200
Subject: [PATCH] Continued to write about hachinohe internship

---
 src/assets/base.css                           |  4 +-
 src/assets/localization/en/localization.json  | 18 +++++-
 src/assets/main.css                           |  2 +-
 .../projects/semanticSegmentationView.vue     | 63 +++++++++++++++++++
 4 files changed, 83 insertions(+), 4 deletions(-)

diff --git a/src/assets/base.css b/src/assets/base.css
index b8655f1..1307eda 100644
--- a/src/assets/base.css
+++ b/src/assets/base.css
@@ -1,7 +1,7 @@
-* {
+/** {
   overflow-y: hidden;
   overflow-x: hidden;
-}
+}*/
 html{
   background-color: #1E1E1E;
 }
diff --git a/src/assets/localization/en/localization.json b/src/assets/localization/en/localization.json
index 9dc64aa..67d046a 100644
--- a/src/assets/localization/en/localization.json
+++ b/src/assets/localization/en/localization.json
@@ -116,7 +116,23 @@
       "content": {
         "context": {
           "title": "Some context before begining...",
-          "text": "The picture you can see next to this text is Hachinohe Kosen, the place were I had the chance to go for my very first research internship. I was sent there for a 3 months stay, with some other French students."
+          "text": "The picture you can see next to this text is Hachinohe Kosen, the place where I had the chance to go for my very first research internship. I was sent there for a 3 months stay, with some other French students."
+        },
+        "semantic": {
+          "title": "Semantic Segmentation models study",
+          "text": "I installed three models on my laptop, Unet, Segnet and DeeplabV3, and trained them using the \"oxford_iiit_pet\" dataset ( from which the pictures on this page come from ). I searched about them and obtained various statistics and results, that I analysed and used to determine which one was the best Semantic Segmentation model back then.",
+          "table": {
+            "model": "Model",
+            "accuracy": "Accuracy",
+            "loss": "Loss",
+            "process_time": "Processing time for one frame",
+            "year": "Release year"
+          },
+          "result": "According to this table, the best model among those I tested was DeeplabV3. It was the one giving the best results according to Accuracy, but is a little slower than Unet."
+        },
+        "human_recognition": {
+          "title": "Human detection",
+          "text": "Along with the Semantic Segmentation, I was asked to find a way to recognize an human from a webcam stream. I chose the model YoloV5 to implement this functionality, and got some nice results."
         }
       }
     }
diff --git a/src/assets/main.css b/src/assets/main.css
index d706a10..7b65bac 100644
--- a/src/assets/main.css
+++ b/src/assets/main.css
@@ -1,6 +1,6 @@
 @import './base.css';
 
-h1,h2,h3,h4,h5,p{
+h1,h2,h3,h4,h5,p,td,th{
   color: #EAEAEA;
   /*Family Roboto or Atma ?*/
 }
diff --git a/src/views/projects/semanticSegmentationView.vue b/src/views/projects/semanticSegmentationView.vue
index 2ddd78b..8d5a0b0 100644
--- a/src/views/projects/semanticSegmentationView.vue
+++ b/src/views/projects/semanticSegmentationView.vue
@@ -11,6 +11,49 @@
         <p>{{ $t("projects.semantic_segmentation.content.context.text") }}</p>
         <img :src="hachinoheKosenImg"/>
       </doubleRowHolder>
+      <h2>{{ $t("projects.semantic_segmentation.content.semantic.title") }}</h2>
+      <p>{{ $t("projects.semantic_segmentation.content.semantic.text") }}</p>
+
+      <div id="table_wrapper">
+        <table>
+          <thead>
+            <tr>
+              <th>{{ $t("projects.semantic_segmentation.content.semantic.table.model") }}</th>
+              <th>{{ $t("projects.semantic_segmentation.content.semantic.table.accuracy") }}</th>
+              <th>{{ $t("projects.semantic_segmentation.content.semantic.table.loss") }}</th>
+              <th>{{ $t("projects.semantic_segmentation.content.semantic.table.process_time") }}</th>
+              <th>{{ $t("projects.semantic_segmentation.content.semantic.table.year") }}</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>Unet</td>
+              <td>0.9233</td>
+              <td>0.1898</td>
+              <td>10 ms</td>
+              <td>2015</td>
+            </tr>
+            <tr>
+              <td>Segnet</td>
+              <td>0.9681</td>
+              <td>0.07575</td>
+              <td>100 ms</td>
+              <td>2016</td>
+            </tr>
+            <tr>
+              <td>DeeplabV3</td>
+              <td>0.9708</td>
+              <td>0.06759</td>
+              <td>15 ms</td>
+              <td>2017</td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+      <p>{{ $t("projects.semantic_segmentation.content.semantic.result") }}</p>
+
+      <h2>{{ $t("projects.semantic_segmentation.content.human_recognition.title") }}</h2>
+      <p>{{ $t("projects.semantic_segmentation.content.human_recognition.text") }}</p>
     </article>
   </template>
 
@@ -50,5 +93,25 @@
     min-width: 200px;
     max-width: 400px;
   }
+
+  #table_wrapper{
+    margin-top: 20px;
+    margin-bottom: 15px;
+    width: 80%;
+    min-width: 250px;
+    overflow-x: auto !important;
+  }
+  th{
+    font-size:1.3em;
+    padding-left: 5px;
+    padding-right: 5px;
+  }
+  table,th,td{
+    border: solid 1px white;
+    border-collapse: collapse;
+  }
+  table{
+    margin: auto;
+  }
 </style>
   
\ No newline at end of file