adding changes for submission

Dockerfile

@@ -60,6 +60,9 @@ RUN export uid=${HOST_UID} gid=${HOST_GID} && \
 
 USER ${USER_NAME}
 WORKDIR ${HOME_DIR}
+
+# Custom changes for Submission
+
 COPY ./requirements.txt .
 RUN pip install -r requirements.txt --user
 COPY . .

README.md

 ![CrowdAI-Logo](https://github.com/crowdAI/crowdai/raw/master/app/assets/images/misc/crowdai-logo-smile.svg?sanitize=true)
 
-# crowdAI Mapping Challenge : Baseline
+# crowdAI Food Challenge : Baseline
 
-This repository contains the details of implementation of the Baseline submission using [Mask RCNN](https://arxiv.org/abs/1703.06870) which obtains a score of `[AP(IoU=0.5)=0.697 ; AR(IoU=0.5)=0.479]` for the [crowdAI Mapping Challenge](https://www.crowdai.org/challenges/mapping-challenge).
+This repository contains the details of implementation of the Baseline submission using [Mask RCNN](https://arxiv.org/abs/1703.06870) which obtains a score of `[AP(IoU=0.5)=0.183 ; AR(IoU=0.5)=0.224]` for the [AICrowd Food Challenge](https://www.aicrowd.com/challenges/food-recognition-challenge).
 
 # Installation
 ```
-git clone https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
-cd crowdai-mapping-challenge-mask-rcnn
+git clone https://gitlab.aicrowd.com/nikhil_rayaprolu/food-recognition/
+cd food-recognition
 # Please ensure that you use python3.6
 pip install -r requirements.txt
 python setup.py install
 ```
 
 # Notebooks
-Please follow the instructions on the relevant notebooks for the training, prediction and submissions.
+Please follow the instructions on the relevant files for the training, prediction and submissions.
 
-* [Training](Training.ipynb)
-* [Prediction and Submission](Prediction-and-Submission.ipynb)
+* [Training](Training.py)
+* [Prediction and Submission](eval.py)
   (_pre-trained weights for baseline submission included_)
 
-# Results
-![sample_predictions](images/predictions.png)
+# How to Submit
+As noted at https://github.com/AIcrowd/food-recognition-challenge-starter-kit/ this repository contains aicrowd.json and custom changes
+to Dockerfile
+```
+# Custom changes for Submission
+
+COPY ./requirements.txt .
+RUN pip install -r requirements.txt --user
+COPY . .
+```
+changes to [run.py](run.py#L135) can be seen at line 135, where we call a function from [eval.py](eval.py#L82) .
+ 
+run.py . 
+```
+evaluate(test_images_path, predictions_output_path)
+```
+Inside the evaluate function in [eval.py](eval.py#L102), we pass the images into the model for detection . 
+eval.py . 
+```
+predoctions = model.detect(images, verbose=0)
+```
+
+By writing this code, you are basically allowing AICrowd Auto-Evaluators to pass private test data into your model and detect predictions in runtime
+for the new data.
+
+* [Training](train.py)
+* [Prediction and Submission](eval.py)
+  (_pre-trained weights for baseline submission included_)
 
 # Citation
 ```
-@misc{crowdAIMappingChallengeBaseline2018,
-  author = {Mohanty, Sharada Prasanna},
-  title = {CrowdAI Mapping Challenge 2018 : Baseline with Mask RCNN},
-  year = {2018},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn}},
-  commit = {bac1cf19adbc9d078122c6933da6f808c4ee590d}
+@misc{AICrowdFoodChallengeBaseline2018,
+  author = {Nikhil, Rayaprolu},
+  title = {AICrowd Food Recognition Challenge 2019 : Baseline with Mask RCNN},
+  year = {2019},
+  publisher = {Gitlab},
+  journal = {Gitlab repository},
+  howpublished = {\url{https://gitlab.aicrowd.com/nikhil_rayaprolu/food-recognition}},
 }
 ```
 # Acknowledgements
@@ -41,4 +66,4 @@ Many thanks to all the contributors of that project.
 You are encouraged to checkout [https://github.com/matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN) for documentation on many other aspects of this code.
 
 # Author
-Sharada Mohanty [sharada.mohanty@epfl.ch](sharada.mohanty@epfl.ch)
+Nikhil Rayaprolu [nr178@student.london.ac.uk](nr178@student.london.ac.uk)

eval.py

@@ -21,7 +21,7 @@ from pycocotools import mask as maskUtils
 import coco #a slightly modified version
 
 from mrcnn.evaluate import build_coco_results, evaluate_coco
-from mrcnn.dataset import MappingChallengeDataset
+from mrcnn.dataset import FoodChallengeDataset
 from mrcnn import visualize
 
 
@@ -40,7 +40,7 @@ from mrcnn.config import Config
 from mrcnn import model as modellib, utils
 
 
-PRETRAINED_MODEL_PATH = os.path.join(ROOT_DIR,"models/mask_rcnn_crowdai-mapping-challenge_0060.h5")
+PRETRAINED_MODEL_PATH = os.path.join(ROOT_DIR,"models/mask_rcnn_crowdai-food-challenge_0060.h5")
 LOGS_DIRECTORY = os.path.join(ROOT_DIR, "logs")
 MODEL_DIR = os.path.join(ROOT_DIR, "logs")
 # IMAGE_DIR = os.path.join(ROOT_DIR, "data", "test")
@@ -54,7 +54,7 @@ class InferenceConfig(coco.CocoConfig):
     NUM_CLASSES = 41  # 1 Background + 1 Building
     IMAGE_MAX_DIM=320
     IMAGE_MIN_DIM=320
-    NAME = "crowdai-mapping-challenge"
+    NAME = "crowdai-food-challenge"
 config = InferenceConfig()
 config.display()
 

mrcnn/dataset.py

@@ -7,9 +7,9 @@ from pycocotools import mask as maskUtils
 
 import os
 
-class MappingChallengeDataset(utils.Dataset):
+class FoodChallengeDataset(utils.Dataset):
     def load_dataset(self, dataset_dir, load_small=False, return_coco=True):
-        """ Loads dataset released for the crowdAI Mapping Challenge(https://www.crowdai.org/challenges/mapping-challenge)
+        """ Loads dataset released for the AICrowd Food Challenge
             Params:
                 - dataset_dir : root directory of the dataset (can point to the train/val folder)
                 - load_small : Boolean value which signals if the annotations for all the images need to be loaded into the memory,
@@ -37,13 +37,13 @@ class MappingChallengeDataset(utils.Dataset):
 
         # register classes
         for _class_id in classIds:
-            self.add_class("crowdai-mapping-challenge", _class_id, self.coco.loadCats(_class_id)[0]["name"])
+            self.add_class("crowdai-food-challenge", _class_id, self.coco.loadCats(_class_id)[0]["name"])
 
         # Register Images
         for _img_id in image_ids:
             assert(os.path.exists(os.path.join(image_dir, self.coco.imgs[_img_id]['file_name'])))
             self.add_image(
-                "crowdai-mapping-challenge", image_id=_img_id,
+                "crowdai-food-challenge", image_id=_img_id,
                 path=os.path.join(image_dir, self.coco.imgs[_img_id]['file_name']),
                 width=self.coco.imgs[_img_id]["width"],
                 height=self.coco.imgs[_img_id]["height"],
@@ -70,7 +70,7 @@ class MappingChallengeDataset(utils.Dataset):
         """
 
         image_info = self.image_info[image_id]
-        assert image_info["source"] == "crowdai-mapping-challenge"
+        assert image_info["source"] == "crowdai-food-challenge"
 
         instance_masks = []
         class_ids = []
@@ -79,7 +79,7 @@ class MappingChallengeDataset(utils.Dataset):
         # of class IDs that correspond to each channel of the mask.
         for annotation in annotations:
             class_id = self.map_source_class_id(
-                "crowdai-mapping-challenge.{}".format(annotation['category_id']))
+                "crowdai-food-challenge.{}".format(annotation['category_id']))
             if class_id:
                 m = self.annToMask(annotation,  image_info["height"],
                                                 image_info["width"])
@@ -100,7 +100,7 @@ class MappingChallengeDataset(utils.Dataset):
             return mask, class_ids
         else:
             # Call super class to return an empty mask
-            return super(MappingChallengeDataset, self).load_mask(image_id)
+            return super(FoodChallengeDataset, self).load_mask(image_id)
 
 
     def image_reference(self, image_id):
@@ -109,7 +109,7 @@ class MappingChallengeDataset(utils.Dataset):
             Ideally you this function is supposed to return a URL
             but in this case, we will simply return the image_id
         """
-        return "crowdai-mapping-challenge::{}".format(image_id)
+        return "crowdai-food-challenge::{}".format(image_id)
     # The following two functions are from pycocotools with a few changes.
 
     def annToRLE(self, ann, height, width):

train.py

@@ -16,7 +16,7 @@ from pycocotools.cocoeval import COCOeval
 from pycocotools import mask as maskUtils
 
 from mrcnn.evaluate import build_coco_results, evaluate_coco
-from mrcnn.dataset import MappingChallengeDataset
+from mrcnn.dataset import FoodChallengeDataset
 
 import zipfile
 import urllib.request
@@ -32,13 +32,13 @@ from mrcnn import model as modellib, utils
 
 PRETRAINED_MODEL_PATH = os.path.join(ROOT_DIR,"data", "mask_rcnn_coco.h5")
 LOGS_DIRECTORY = os.path.join(ROOT_DIR, "logs")
-class MappingChallengeConfig(Config):
+class FoodChallengeConfig(Config):
     """Configuration for training on data in MS COCO format.
     Derives from the base Config class and overrides values specific
     to the COCO dataset.
     """
     # Give the configuration a recognizable name
-    NAME = "crowdai-mapping-challenge"
+    NAME = "crowdai-food-challenge"
 
     # We use a GPU with 12GB memory, which can fit two images.
     # Adjust down if you use a smaller GPU.
@@ -57,7 +57,7 @@ class MappingChallengeConfig(Config):
     IMAGE_MAX_DIM=256
     IMAGE_MIN_DIM=256
 
-config = MappingChallengeConfig()
+config = FoodChallengeConfig()
 config.display()
 import keras.backend
 
@@ -69,10 +69,10 @@ model_path = PRETRAINED_MODEL_PATH
 model.load_weights(model_path, by_name=True, exclude=[
         "mrcnn_class_logits", "mrcnn_bbox_fc",
         "mrcnn_bbox", "mrcnn_mask"])
-dataset_train = MappingChallengeDataset()
+dataset_train = FoodChallengeDataset()
 dataset_train.load_dataset(dataset_dir=os.path.join("data", "train"), load_small=False)
 dataset_train.prepare()
-dataset_val = MappingChallengeDataset()
+dataset_val = FoodChallengeDataset()
 val_coco = dataset_val.load_dataset(dataset_dir=os.path.join("data", "val"), load_small=False, return_coco=True)
 dataset_val.prepare()
 print("Training network heads")