style: add readme

README.md

@@ -1,75 +1,11 @@
-# School in AI Project Work
-
-This repository contains the code to train and evaluate a pedestrian detector for 
-the "School in Ai 2° edition"@[@UNIMORE](https://www.unimore.it/)
-
-## Installation
-
-N.B.: Installation only avaiable in win64 environments
-
-Create and activate an environment with all required packages:
-
-```
-conda create --name ped_detector --file deps/wins/conda_environment.txt
-# or conda env create -f deps/win/conda_environment.yml
-conda activate cvcspw
-pip install -r deps/win/pip_requirements.txt
-```
-
-## Dataset download and preparation:
-### Solution 1 - From Google Drice
-Download the storage folder directly from Google Drive [here](link google drive)
-and place it in the root dir of the project
-After runnning this step, your storage directory should look like this:
-```text
-storage
-    ├── MOTChallenge
-        ├── MOT17
-        ├── motcha_coco_annotations
-    ├── MOTSynth
-        ├── annotations
-        ├── comb_annotations
-        ├── frames
-    ├── motsynth_output
-```
-### Solution 2 - From scratch
-#### Prepare MOTSynth dataset
-1. Download MOTSynth_1.
-```
-wget -P ./storage/MOTSynth https://motchallenge.net/data/MOTSynth_1.zip
-unzip ./storage/MOTSynth/MOTSynth_1.zip
-rm ./storage/MOTSynth/MOTSynth_1.zip
-```
-2. Delete video from 123 to 256
-2. Extract frames from the videos
-```
-python tools/anns/to_frames.py --motsynth-root ./storage/MOTSynth
-
-# now you can delete other videos
-rm -r ./storage/MOTSynth/MOTSynth_1
-```
-3. Download and extract annotations
-```
-wget -P ./storage/MOTSynth https://motchallenge.net/data/MOTSynth_coco_annotations.zip
-unzip ./storage/MOTSynth/MOTSynth_coco_annotations.zip
-rm ./storage/MOTSynth/MOTSynth_coco_annotations.zip
-```
-4. Prepare combined annotations for MOTSynth from the original coco annotations
-```
-python tools/anns/combine_anns.py --motsynth-path ./storage/MOTSynth
-```
-#### Prepare MOT17 dataset
-
-
-## Colab Usage
-
-You can also use [Google Colab](https://colab.research.google.com) if you need remote resources like GPUs.
-In the notebook folder you can find some useful .ipynb files and remember to load the storage folder in your GDrive before usage.
-
-## Object Detection
-
-An adaption of torchvision's detection reference code is done to train Faster R-CNN on a portion of the MOTSynth dataset. To train the model you can run:
-```
-./scripts/train_detector
-```
-
+---
+title: School in AI Project Work
+emoji: 🐨
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.9
+app_file: app.py
+pinned: false
+license: mit
+---