update custom dataset tutorial

assets/CustomDatasetTutorial.md

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+# Tutorial for finetuning on a custom dataset
+
+## Step 1. Convert the custom dataset to coco format.
+
+- The coco format can be found in [Link](https://cocodataset.org/#format-data). Specifically, the coco annotation file (`.json`) includes three necessary feilds, *i.e.*, image, annotation, categories. A toy sample (named `toy_sample.json`) is provided below:
+
+```json
+{
+  "categories": 
+  [{
+      "supercategory": "person", 
+      "id": 1, 
+      "name": "person"
+  }], 
+ "images": 
+  [{
+      "license": 1, 
+      "file_name": "000000425226.jpg",        
+      "coco_url": "http://images.cocodataset.org/val2017/000000425226.jpg", 
+      "height": 640, 
+      "width": 480, 
+      "date_captured": 
+      "2013-11-14 21:48:51", 
+      "flickr_url": 
+      "http://farm5.staticflickr.com/4055/4546463824_bc40e0752b_z.jpg", 
+      "id": 1
+  }], 
+ "annotations": 
+  [{
+      "image_id": 1, 
+      "category_id": 1, 
+      "segmentation": [], 
+      "area": 47803.279549999985, 
+      "iscrowd": 0, 
+      "bbox": [73.35, 206.02, 300.58, 372.5], 
+      "id": 1
+  }]
+}
+```
+
+- Then we can organize the custom dataset (including images and annotations) as follows:
+```
+├── Custom_coco
+│   ├── annotations
+│   │   └── toy_sample.json
+│   ├── images
+│   │   └── 000000425226.jpg
+```
+
+## Step2. Link custom dataset into DAMO-YOLO
+- Link your dataset into `datasets`. 
+```
+ln -s path/to/Custom_coco datasets/toy_sample
+```
+
+- Add the custom dataset into `damo/config/paths_catalog.py`. Note, the added dataset should contain **coco** in their names to declare the dataset format, *e.g.*, here we use `sample_train_coco` and `sample_test_coco`.
+```
+'sample_train_coco': {
+    'img_dir': 'toy_sample/images',
+    'ann_file': 'toy_sample/annotations/toy_sample.json'
+},
+'sample_test_coco': {
+    'img_dir': 'toy_sample/images',
+    'ann_file': 'toy_sample/annotations/toy_sample.json'
+},
+```
+
+
+## Step3. Modify the config file.
+In this tutorial, we finetune on DAMO-YOLO-Tiny as example.
+- Download the DAMO-YOLO-Tiny torch model from [Model Zoo](https://github.com/tinyvision/DAMO-YOLO#Model-Zoo)
+- Add the following pretrained model path into `damoyolo_tinynasL20_T.py`.
+```
+self.train.finetune_path='path/to/damoyolo_tinynasL20_T.pth'
+```
+- Modify the custom dataset in config file. Change `coco_2017_train` and `coco_2017_test` in `damoyolo_tinynasL20_T.py` to `sample_train_coco` and `sample_test_coco` respectively.
+https://github.com/tinyvision/DAMO-YOLO/blob/6e38813220900955d0f6138429c91a33a79c922f/configs/damoyolo_tinynasL20_T.py#L33-L34
+
+- Modify the category number in config file. Change `'num_classes': 80` in `damoyolo_tinynasL20_T.py` to `'num_classes': 1`. Because in our toy sample, there is only one category, so we set `num_classes` to 1.
+https://github.com/tinyvision/DAMO-YOLO/blob/6e38813220900955d0f6138429c91a33a79c922f/configs/damoyolo_tinynasL20_T.py#L64-L66
+
+## Step4. Finetune on custom dataset
+You can run the finetuning with the following code:
+```
+python -m torch.distributed.launch --nproc_per_node=8 tools/train.py -f configs/damoyolo_tinynasL20_T.py
+```
+