Internal change

PiperOrigin-RevId: 464175568

official/projects/waste_identification_ml/pre_processing/config/data/material_form_labels.pbtxt

+
+item {
+id:1
+name:'Flexibles'
+}
+item {
+id:2
+name:'Bottle'
+}
+item {
+id:3
+name:'Jar'
+}
+item {
+id:4
+name:'Carton'
+}
+item {
+id:5
+name:'Sachets-&-Pouch'
+}
+item {
+id:6
+name:'Blister-pack'
+}
+item {
+id:7
+name:'Tray'
+}
+item {
+id:8
+name:'Tube'
+}
+item {
+id:9
+name:'Can'
+}
+item {
+id:10
+name:'Tub'
+}
+item {
+id:11
+name:'Cosmetic'
+}
+item {
+id:12
+name:'Box'
+}
+item {
+id:13
+name:'Clothes'
+}
+item {
+id:14
+name:'Bulb'
+}
+item {
+id:15
+name:'Cup-&-glass'
+}
+item {
+id:16
+name:'Book-&-magazine'
+}
+item {
+id:17
+name:'Bag'
+}
+item {
+id:18
+name:'Lid'
+}
+item {
+id:19
+name:'Clamshell'
+}
+item {
+id:20
+name:'Mirror'
+}
+item {
+id:21
+name:'Tangler'
+}
+item {
+id:22
+name:'Cutlery'
+}
+item {
+id:23
+name:'Cassette-&-tape'
+}
+item {
+id:24
+name:'Electronic-devices'
+}
+item {
+id:25
+name:'Battery'
+}
+item {
+id:26
+name:'Pen-&-pencil'
+}
+item {
+id:27
+name:'Paper-products'
+}
+item {
+id:28
+name:'Foot-wear'
+}
+item {
+id:29
+name:'Scissor'
+}
+item {
+id:30
+name:'Toys'
+}
+item {
+id:31
+name:'Brush'
+}
+item {
+id:32
+name:'Pipe'
+}
+item {
+id:33
+name:'Foil'
+}
+item {
+id:34
+name:'Hangers'
+}

official/projects/waste_identification_ml/pre_processing/config/data/material_labels.pbtxt

+
+item {
+id:1
+name:'Inorganic-waste'
+}
+item {
+id:2
+name:'Textiles'
+}
+item {
+id:3
+name:'Rubber-and-Leather'
+}
+item {
+id:4
+name:'Wood'
+}
+item {
+id:5
+name:'Food'
+}
+item {
+id:6
+name:'Plastics'
+}
+item {
+id:7
+name:'Yard-trimming'
+}
+item {
+id:8
+name:'Fiber'
+}
+item {
+id:9
+name:'Glass'
+}
+item {
+id:10
+name:'Metals'
+}

official/projects/waste_identification_ml/pre_processing/config/data/plastic_type_labels.pbtxt

+
+item {
+id:1
+name:'HDPE'
+}
+item {
+id:2
+name:'PETE'
+}
+item {
+id:3
+name:'LDPE'
+}
+item {
+id:4
+name:'PS'
+}
+item {
+id:5
+name:'PP'
+}
+item {
+id:6
+name:'PVC'
+}
+item {
+id:7
+name:'Others-MLP'
+}
+item {
+id:8
+name:'Others-Tetrapak'
+}
+item {
+id:9
+name:'Others-HIPC'
+}

official/projects/waste_identification_ml/pre_trained_models.md

@@ -20,18 +20,3 @@ Mask RCNN is a state-of-art deep learning model for instance image segmentation,
 | Material Model | Resnet | saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/material_model.zip) |
 | Material Form model | Resnet | saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/material_form_model.zip) |
 |Plastic model | Resnet| saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/plastic_types_model.zip) |
-
-## Installation & Download
-
-You need to use gsutil command line tool to download the deep learning weights from their respective GCP buckets.
-- Start by logging into the [Google Cloud Console and create a project](https://developers.google.com/workspace/guides/create-project).
-- Download and install the Google Cloud SDK from the [official website](https://cloud.google.com/sdk/docs/#mac).
-- You can verity that the install went successfully by opening up a machine terminal and executing the command 
-```sh
-gsutil -v
-```
-- Once your install is successfull you can download the weight from the GCP buckets using gsutil command. For example -
-```sh
-gsutil -m cp -r gs://official_release/version_1/plastic_types_model/saved_model/* .
-```
-- The above command will download the weights in your local directory.