For every module-type tag, add a task tag (#55)

* For every module-type tag, add a task tag

assets/docs/agripredict/disease-classification/1.md

@@ -3,6 +3,7 @@ Fine-grained image classification model for crop disease classification.
 
 <!-- asset-path: https://agripredict-tfub.s3.amazonaws.com/model.tar.gz -->
 <!-- module-type: image-classification -->
+<!-- task: image-classification -->
 <!-- network-architecture: mobilenet -->
 <!-- dataset: internal-agripredict-dataset -->
 <!-- language: en -->

assets/docs/bohemian-visual-recognition-alliance/models/mushroom-identification_v1/1.md

@@ -6,6 +6,7 @@ Image Classification model for mushroom species identification.
 <!-- format: saved_model -->
 <!-- asset-path: http://ptak.felk.cvut.cz/personal/picekluk/DanishSvampeAtlas/Inception_v4_saved_model_1.tar.gz -->
 <!-- module-type: image-classification -->
+<!-- task: image-classification -->
 <!-- network-architecture: inception-v4 -->
 <!-- license: MIT -->
 <!-- interactive-model-name: vision -->

assets/docs/bohemian-visual-recognition-alliance/models/mushroom-identification_v1/2.md

@@ -6,6 +6,7 @@ Lightweights Image Classification model for mushroom species identification.
 <!-- format: saved_model -->
 <!-- asset-path: http://ptak.felk.cvut.cz/personal/picekluk/DanishSvampeAtlas/MobileNet_v3_360_saved_model_2.tar.gz -->
 <!-- module-type: image-classification -->
+<!-- task: image-classification -->
 <!-- network-architecture: mobilenet-v3 -->
 <!-- license: MIT -->
 <!-- interactive-model-name: vision -->

assets/docs/captain-pool/esrgan-tf2/1.md

@@ -6,6 +6,7 @@ Works best on Bicubically downsampled images.\
 
 <!-- asset-path: legacy -->
 <!-- module-type: image-super-resolution -->
+<!-- task: image-super-resolution -->
 <!-- network-architecture: gan -->
 <!-- dataset: div2k -->
 <!-- language: en -->

assets/docs/digitalepidemiologylab/models/covid-twitter-bert/1.md

@@ -3,6 +3,7 @@ BERT-large-uncased model, pretrained on a corpus of messages from Twitter about
 
 <!-- asset-path: https://crowdbreaks-public.s3.eu-central-1.amazonaws.com/models/covid-twitter-bert/v1/tfhub/covid-twitter-bert-v1.tar.gz -->
 <!-- module-type: text-embedding -->
+<!-- task: text-embedding -->
 <!-- network-architecture: transformer -->
 <!-- dataset: twitter -->
 <!-- language: en -->

assets/docs/digitalepidemiologylab/models/covid-twitter-bert/2.md

@@ -3,6 +3,7 @@ BERT-large-uncased model, pretrained on a corpus of messages from Twitter about
 
 <!-- asset-path: https://crowdbreaks-public.s3.eu-central-1.amazonaws.com/models/covid-twitter-bert/v2/tfhub/covid-twitter-bert-v2.tar.gz -->
 <!-- module-type: text-embedding -->
+<!-- task: text-embedding -->
 <!-- network-architecture: transformer -->
 <!-- dataset: twitter -->
 <!-- language: en -->

assets/docs/emilutz/collections/vgg19-unpooling-encoder-decoder/1.md

@@ -4,6 +4,7 @@ Collection of image encoders and decoders trained on the COCO 2017 dataset where
 
 <!-- dataset: coco-2017 -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- network-architecture: vgg-style -->
 
 ## Overview

assets/docs/emilutz/models/vgg19-block1-conv2-unpooling-decoder/1.md

@@ -4,6 +4,7 @@ Image decoder based on vgg19 that uses unpooling layers for upsampling.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block1-conv2/decoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block1-conv2-unpooling-encoder/1.md

@@ -4,6 +4,7 @@ Image encoder based on vgg19 that stores argmax values for maxpool layers.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block1-conv2/encoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block2-conv2-unpooling-decoder/1.md

@@ -4,6 +4,7 @@ Image decoder based on vgg19 that uses unpooling layers for upsampling.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block2-conv2/decoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block2-conv2-unpooling-encoder/1.md

@@ -4,6 +4,7 @@ Image encoder based on vgg19 that stores argmax values for maxpool layers.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block2-conv2/encoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block3-conv2-unpooling-decoder/1.md

@@ -4,6 +4,7 @@ Image decoder based on vgg19 that uses unpooling layers for upsampling.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block3-conv2/decoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block3-conv2-unpooling-encoder/1.md

@@ -4,6 +4,7 @@ Image encoder based on vgg19 that stores argmax values for maxpool layers.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block3-conv2/encoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block4-conv2-unpooling-decoder/1.md

@@ -4,6 +4,7 @@ Image decoder based on vgg19 that uses unpooling layers for upsampling.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block4-conv2/decoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block4-conv2-unpooling-encoder/1.md

@@ -4,6 +4,7 @@ Image encoder based on vgg19 that stores argmax values for maxpool layers.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block4-conv2/encoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block5-conv2-unpooling-decoder/1.md

@@ -4,6 +4,7 @@ Image decoder based on vgg19 that uses unpooling layers for upsampling.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block5-conv2/decoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/emilutz/models/vgg19-block5-conv2-unpooling-encoder/1.md

@@ -4,6 +4,7 @@ Image encoder based on vgg19 that stores argmax values for maxpool layers.
 
 <!-- asset-path: https://storage.googleapis.com/vgg19-with-unpooling/vgg19-block5-conv2/encoder.tar.gz -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- fine-tunable: true -->
 <!-- dataset: coco-2017 -->
 <!-- format: saved_model_2 -->

assets/docs/inaturalist/models/vision/embedder/inaturalist_V2/1.md

@@ -5,6 +5,7 @@ Image embedding model trained on the iNaturalist dataset.
 <!-- asset-path: legacy -->
 <!-- format: hub -->
 <!-- module-type: image-feature-vector -->
+<!-- task: image-feature-vector -->
 <!-- interactive-model-name: inat -->
 <!-- license: CC-BY-NC-4.0 -->
 

assets/docs/intel/midas/v2/1/1.md

@@ -3,6 +3,7 @@ Convolutional neural network for monocular depth estimation from a single RGB im
 
 <!-- asset-path: legacy -->
 <!-- module-type: image-depth-estimation -->
+<!-- task: image-depth-estimation -->
 <!-- network-architecture: midas -->
 <!-- dataset: diml-indoor -->
 <!-- dataset: megadepth -->

assets/docs/intel/midas/v2/1/2.md

@@ -3,6 +3,7 @@ Convolutional neural network for monocular depth estimation from a single RGB im
 
 <!-- asset-path: legacy -->
 <!-- module-type: image-depth-estimation -->
+<!-- task: image-depth-estimation -->
 <!-- network-architecture: midas -->
 <!-- dataset: diml-indoor -->
 <!-- dataset: megadepth -->

assets/docs/intel/midas/v2_1_small/1/1.md

@@ -3,6 +3,7 @@ Mobile convolutional neural network for monocular depth estimation from a single
 
 <!-- asset-path: legacy -->
 <!-- module-type: image-depth-estimation -->
+<!-- task: image-depth-estimation -->
 <!-- network-architecture: midas -->
 <!-- dataset: diml-indoor -->
 <!-- dataset: megadepth -->

assets/docs/metmuseum/models/vision/classifier/imet_attributes_V1/1.md

@@ -5,6 +5,7 @@ Image attribute classifier trained on the iMet Collection 2019 dataset.
 <!-- dataset: imet-collection-2019 -->
 <!-- asset-path: legacy -->
 <!-- module-type: image-classification-logits-->
+<!-- task: image-classification-logits-->
 <!-- network-architecture: inception-v4 -->
 <!-- fine-tunable: false -->
 <!-- format: hub -->

assets/docs/monatis/models/german-mbmelgan/1.md

@@ -3,6 +3,7 @@ German Multi-band MelGAN as described in [1].
 
 <!-- asset-path: https://storage.googleapis.com/mys-released-models/german-tts-mbmelgan.tar.gz -->
 <!-- module-type: audio-synthesis -->
+<!-- task: audio-synthesis -->
 <!-- network-architecture: multiband-melgan -->
 <!-- dataset: thorsten -->
 <!-- language: de -->

assets/docs/monatis/models/german-tacotron2/1.md

@@ -3,6 +3,7 @@ German Tacotron 2 as described in [1].
 
 <!-- asset-path: https://storage.googleapis.com/mys-released-models/german-tts-tacotron2.tar.gz -->
 <!-- module-type: text-to-mel -->
+<!-- task: text-to-mel -->
 <!-- network-architecture: tacotron2 -->
 <!-- dataset: thorsten -->
 <!-- language: de -->

assets/docs/nascarr/models/nq/1.md

@@ -4,6 +4,7 @@ Question Answering model trained on Natural Questions Dataset [1, 2].
 
 <!-- asset-path: https://github.com/nascarr/natural-questions/raw/master/models/v1/model.tar.gz -->
 <!-- module-type: text-question-answering -->
+<!-- task: text-question-answering -->
 <!-- network-architecture: transformer -->
 <!-- dataset: natural-questions -->
 <!-- language: en -->

assets/docs/nvidia/models/unet/industrial/class_1/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_1/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_10/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_10/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_2/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_2/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_3/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_3/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_4/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_4/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_5/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_5/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_6/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_6/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_7/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_7/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_8/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_8/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/nvidia/models/unet/industrial/class_9/1.md

@@ -3,6 +3,7 @@ Convolutional auto-encoder for 2D image segmentation.
 
 <!-- asset-path: https://developer.download.nvidia.com/compute/redist/Binary_Files/unet_tfhub_modules/class_9/1.tar.gz -->
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: unet -->
 <!-- dataset: dagm2007 -->
 <!-- fine-tunable: false  -->

assets/docs/prvi/tf2-nq/1.md

@@ -3,6 +3,7 @@ Question Answering model for Kaggle TensorFlow 2.0 Question Answering challenge
 
 <!-- asset-path: https://hpz400.cs.elte.hu/model/model.tar.gz -->
 <!-- module-type: text-question-answering -->
+<!-- task: text-question-answering -->
 <!-- network-architecture: transformer -->
 <!-- dataset: natural-questions -->
 <!-- language: en -->

assets/docs/rishit-dagli/mirnet-tfjs/1.md

@@ -2,6 +2,7 @@
 MIRNet model to enhance a low-light images.
 
 <!-- module-type: image-super-resolution -->
+<!-- task: image-super-resolution -->
 <!-- network-architecture: other -->
 <!-- dataset: lol -->
 <!-- fine-tunable: false -->

assets/docs/rishit-dagli/plant-disease/1.md

@@ -2,6 +2,7 @@
 Model to classify among 38 plant disease categories.
 
 <!-- module-type: image-classification -->
+<!-- task: image-classification -->
 <!-- network-architecture: mobilenet -->
 <!-- dataset: plant-village -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/arbitrary-image-stylization-inceptionv3-dynamic-shapes/1.md

@@ -3,6 +3,7 @@ Fast arbitrary image style transfer based on an InceptionV3 backbone.
 
 <!-- dataset: multiple -->
 <!-- module-type: image-style-transfer -->
+<!-- task: image-style-transfer -->
 <!-- network-architecture: other -->
 <!-- fine-tunable: false -->
 <!-- license: Apache-2.0 -->

assets/docs/sayakpaul/arbitrary-image-stylization-inceptionv3/1.md

@@ -3,6 +3,7 @@ Fast arbitrary image style transfer based on an InceptionV3 backbone.
 
 <!-- dataset: multiple -->
 <!-- module-type: image-style-transfer -->
+<!-- task: image-style-transfer -->
 <!-- network-architecture: other -->
 <!-- fine-tunable: false -->
 <!-- language: en -->

assets/docs/sayakpaul/boundless_quarter/1.md

@@ -3,6 +3,7 @@ TFLite quantized GAN-model for image extrapolation.
 
 <!-- dataset: multiple -->
 <!-- module-type: image-style-transfer -->
+<!-- task: image-style-transfer -->
 <!-- network-architecture: other -->
 <!-- fine-tunable: false -->
 <!-- license: Apache-2.0 -->

assets/docs/sayakpaul/cartoongan/1.md

@@ -3,6 +3,7 @@ CartoonGAN model to cartoonize a given image.
 
 <!-- dataset: multiple -->
 <!-- module-type: image-style-transfer -->
+<!-- task: image-style-transfer -->
 <!-- network-architecture: other -->
 <!-- fine-tunable: false -->
 <!-- license: Apache-2.0 -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2-ade20k/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-ade20k-train -->
 <!-- dataset: ade20k -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2-float16/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2-int8/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2_dm05-float16/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-dm05-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2_dm05-int8/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-dm05-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->

assets/docs/sayakpaul/deeplabv3-mobilenetv2_dm05/1.md

@@ -2,6 +2,7 @@
 Lightweight deep learning model for semantic image segmentation.
 
 <!-- module-type: image-segmentation -->
+<!-- task: image-segmentation -->
 <!-- network-architecture: deeplab-mobilenetv2-dm05-coco-voc-trainval -->
 <!-- dataset: pascal-voc-2012 -->
 <!-- fine-tunable: false -->