From d5cbf90ae9399d4364c146db6f473728d5b4485b Mon Sep 17 00:00:00 2001
From: Guillaume Duret <guillaume.duret@ec-lyon.fr>
Date: Wed, 14 Jun 2023 19:17:54 +0000
Subject: [PATCH] minor fixes

---
 README.md | 19 +------------------
 1 file changed, 1 insertion(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 9d90124..29cfbf3 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 
-## FruitBIn
+## FruitBin
 
 The early released dataset can be accesible to the link : https://datasets.liris.cnrs.fr/fruitbin-version1
 
@@ -165,23 +165,6 @@ The compute step takes the rearranged data as input and processes it for future
 The table below provides information about the various generated features. Furthermore, this processed data takes into account the filtering parameters specified in the main.py script, such as the desired level of occlusion. Regarding FruitBin, the scene scenarios are divided into 6000 scenes for training, 2000 scenes for evaluation, and 10000 scenes for testing. In addition, 9 cameras are allocated for training, while 3 cameras are assigned for evaluation and testing, resulting in a total of 15 cameras.
 
 
-Fruit_i	The fruit category being considered
-Meta_Gen	Metadata describing fruit-specific information such as Scene ID, Camera ID, a list of instance IDs related to the fruit, and associated occlusion rates
-BBox	2D bounding boxes
-Bbox_3d_Gen	3D bounding boxes
-Depth_Gen	Depth map data with a resolution of 1280x720
-Depth_resized	Resized depth map data with a resolution of 640x480 for training
-FPS	Farthest Point Sampling (FPS) key points for the 1280x720 image used in Pvnet
-FPS_resized	Resized FPS data with a resolution of 640x480 for training in Pvnet
-Instance_Mask	Instance mask data with a resolution of 1280x720
-Instance_Mask_resized	Resized instance mask data with a resolution of 640x480 for training
-Labels	Instance mask in the Yolov8 format (generated using the 'compute label' script explained below)
-Models	Meshes of the 8 fruits in a common PLY format
-Pose_transformed	6D pose annotations in the PVNet format
-RGB_Gen	RGB image data with a resolution of 1280x720
-RGB_resized	Resized RGB image data with a resolution of 640x480
-Splitting	This folder is only available when the dataset is downloaded online. It contains a list of .txt splitting files for different scenarios, describing the train/eval/test split.
-
 | Parameters |                  Description    |
 | :---:   | :---: | 
 | Fruit_i | The fruit category being considered        |          
-- 
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