From 886ace4c3126965e8f112e73a7517d30461d8918 Mon Sep 17 00:00:00 2001
From: guis98 <guis98@mi.fu-berlin.de>
Date: Mon, 12 Aug 2024 13:11:55 +0200
Subject: [PATCH] files added

---
 README.md | 87 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 48 insertions(+), 39 deletions(-)

diff --git a/README.md b/README.md
index 9ed182c..ec0f76f 100644
--- a/README.md
+++ b/README.md
@@ -1,54 +1,63 @@
-0. Directory structure:
-    Hen Tracking
-    |_data
-    |   |_frames
-    |   |   |_frame_0.jpg, frame_1.jpg, ..., frame_204.jpg
-    |   |_video
-    |       |_video.mkv
-    |
-    |_train
-    |   |_images
-    |   |   |_ first 190 images
-    |   |_labels 
-    |
-    |_test
-    |   |_images
-    |   |   |_ remaining 20 images
-    |   |_labels
-    |
-    |_annotate.py
-    |
-    |_get_stuff.py
-    |
-    |_track.py
-    |
-    |_data.yaml
-
-
-1. Annotated the frames using `ultralytics auto annotator`
-
--> Run `python annotate.py` (See output annotate_output.txt)
+# 0. Directory structure:
+
+```
+Hens Tracking
+
+├── data
+│   ├── frames
+│   │   └── frame_0.jpg, frame_1.jpg, ..., frame_204.jpg
+│   └── video
+│       └──  video.mkv
+│
+├──  train
+│   ├── images
+│   │   └── first 185 images
+│   └── labels
+│
+├──  val
+│   ├── images
+│   │   └── remaining 20 images
+│   └── labels
+│
+├──  annotate.py
+│
+├──  get_stuff.py
+│
+├──  track.py
+│
+└── data.yaml
+```
+
+# 1. Annotated the frames using `ultralytics auto annotator`
+
+> Run `python annotate.py` 
+
+(See output `annotate_output.txt`)
 
 This creates a folder under `data` named 'frames_auto_annotate_labels' and gives a .txt file for each frame containing the segmentations of the hens it detected.
 
 
-2. Put the labels into a proper structure for training i.e. specify custom class (hen)
+# 2. Put the labels into a proper structure for training i.e. specify custom class (hen)
 
--> Run `python get_stuff.py`
+> Run `python get_stuff.py`
 
-This will put all the necesaary files under `train/labels`. Move the last 20 files to `val/labels` for validation in the next stripped
+This will put all the necesaary files under `train/labels`. Move the last 20 files to `val/labels` for validation in the next stripped.
 
 
-3. The model is now trained using a pretrained 'yolov8x-seg'. Ensure that the directory path is good
+# 3. The model is now trained using a pretrained `yolov8x-seg` 
+Ensure that the directory path is good
 
--> Run `yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640` in a terminal opened in the directory
-(See output train_output.txt)
+> Run `yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640` in a terminal opened in the directory
+
+(See output `train_output.txt`)
 
 This will create a `runs` folder and all the metrics and model weights will be stored there.
 
 
-4. Use the best weights as input model for tracking
+# 4. Use the best weights as input model for tracking
+
+> Run `python track.py` 
 
--> Run `python track.py` (See output output_track.py, uncomment line no. 34 if you want to visualize the output alongside running)
+(See `output track_output.txt`, uncomment line no. 34 if you want to visualize the output alongside running)
 
-The final tracking video would be saved under the directory
\ No newline at end of file
+The final tracking video would be saved under the directory.
\ No newline at end of file
-- 
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