uploading DATA.md with data creation instructions

DATA.md

@@ -0,0 +1,85 @@
+## Data Prepration
+
+We use two main classes for datasets ingestion, the first is `.data/dataset_manager.py`, 
+which decodes a `dataset_config.yaml` file, and registers the datasets towards training and testing. 
+The second is `./data/dataset_mapper.py`, which converts the annotations for model propagation and evaluation.
+
+Here's an example for a data configuration:
+```yaml
+# ./data_configs/data_config_pretrain.yaml
+
+ROOT: /data  # This is the root directory where the dataset directories are found, each has the dataset images 
+             # and an annotations.json file
+
+DATASETS:     # These are datasets we converted to COCO format and used for training
+  - SynthText_coco
+  - totaltext_train_coco
+
+VAL_DATASETS:  # Validation is performed on these datasets
+  - totaltext_test_coco
+
+```
+
+In each dataset directory, the model expected to find the images and an `annotations.json` in [COCO format](https://cocodataset.org/)
+according to the following structure:
+
+```json
+{
+  "info": {},
+  "licenses": [{
+      "id": 1,
+      "name": "<License Name>",
+      "url": "<License URL>"
+    }],
+  "categories": [{
+      "id": 1,
+      "name": "word", 
+      "supercategory": "documents"
+  }],
+  "type": "instances",
+  "images": [ 
+    {
+      "id": 1,
+      "file_name": "img199.jpg",
+      "width": 2593,
+      "height": 1936,
+      "date_captured": "<date captured>",
+      "license": 1,
+      "coco_url": "",
+      "url": ""
+    },
+    ...
+  ],
+  "annotations": [
+    {"id": 2544, 
+      "category_id": 1, 
+      "category": "word", 
+      "image_id": 300, 
+      "word_length": 3, 
+      "text": "ROC", 
+      "iscrowd": 0, 
+      "area": 666.5, 
+      "bbox": [47.0, 111.0, 45.0, 25.0], 
+      "segmentation": [[53.0, 113.0, 71.0, 118.0, 85.0, 111.0, 92.0, 126.0, 69.0, 136.0, 47.0, 126.0]], 
+      "width": 360, 
+      "height": 162, 
+      "angle": 1.49, 
+      "orientation": 0, 
+      "rotated_box": [[46.21, 113.42], [91.04, 110.62], [92.53, 134.53], [47.71, 137.33]]}]}
+    ...
+  ]
+}
+
+```
+
+We note that we enrich the baseline COCO annotations with additional fields, including:
+
+```yaml
+    'text': 'Hello'   # A string containing the transcription of the annotation in UTF-8 format
+    'text_length': 5  # The length of the text
+    'angle' 5.5       # The rotated box angle measured in CCW degrees 
+    'orientation' 0 / 1 / 2 / 3  # This is computed by the equation "orientation = (angle + 45) // 90 % 4"
+    'rotated_box:     # (2 x 4 float list), measured in absolute image pixels and degrees, marking the coordinates of the box
+```
+
+

README.md

@@ -53,7 +53,7 @@ pip install -e .
 First, please download any of the following models:
 
 1. Pretrained + fine-tuned on ICDAR'15: [model](https://glass-text-spotting.s3.eu-west-1.amazonaws.com/models/glass_250k_icdar15_fintune.pth)
-2. Pretrained + fine-tuned on TotalText: [model](https://glass-text-spotting.s3.eu-west-1.amazonaws.com/models/glass_250k_full_textocr_finetune.pth)
+2. Pretrained + fine-tuned on TotalText: [model](https://glass-text-spotting.s3.eu-west-1.amazonaws.com/models/glass_250k_totaltext_finetune.pth)
 3. Pretrained + fine-tuned on all datasets, inc. TextOCR: [model](https://glass-text-spotting.s3.eu-west-1.amazonaws.com/models/glass_250k_full_textocr_finetune.pth)
 
 All of these models can be run together with the default pre-training config