InstanceRefer | Instance Multi-level Contextual Referring

InstanceRefer

InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

This repository is for the ICCV 2021 paper and 1st method on ScanRefer benchmark [arxiv paper].

Zhihao Yuan, Xu Yan, Yinghong Liao, Ruimao Zhang, Zhen Li*, Shuguang Cui

If you find our work useful in your research, please consider citing:

@article{yuan2021instancerefer,
  title={Instancerefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring},
  author={Yuan, Zhihao and Yan, Xu and Liao, Yinghong and Zhang, Ruimao and Li, Zhen and Cui, Shuguang},
  journal={ICCV},
  year={2021}
}

News

• 2021-07-23 InstanceRefer is accepted at ICCV 2021 :fire:!
• 2021-04-22 We release evaluation codes and pre-trained model!
• 2021-03-31 We release InstanceRefer v1 :rocket:!
• 2021-03-18 We achieve 1st place in ScanRefer leaderboard :

Getting Started

Setup

The code is tested on Ubuntu 16.04 LTS & 18.04 LTS with PyTorch 1.6 CUDA 10.1 and PyTorch 1.8 CUDA 10.2 installed.

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

Install the necessary packages listed out in requirements.txt:

pip install -r requirements.txt

After all packages are properly installed, please run the following commands to compile the torchsaprse 1.2:

pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git
sudo apt-get install libsparsehash-dev

Before moving on to the next step, please don't forget to set the project root path to the CONF.PATH.BASE in lib/config.py.

Data preparation

1. Download the ScanRefer dataset and unzip it under data/.
2. Downloadand the preprocessed GLoVE embeddings (~990MB) and put them under data/.
3. Download the ScanNetV2 dataset and put (or link) scans/ under (or to) data/scannet/scans/ (Please follow the ScanNet Instructions for downloading the ScanNet dataset). After this step, there should be folders containing the ScanNet scene data under the data/scannet/scans/ with names like scene0000_00
4. Used official and pre-trained PointGroup generate panoptic segmentation in PointGroupInst/. We provide pre-processed data in Baidu Netdisk [password: 0nxc].
5. Pre-processed instance labels, and new data should be generated in data/scannet/pointgroup_data/Finally, the dataset folder should be organized as follows.

Training

Train the InstanceRefer model. You can change hyper-parameters in config/InstanceRefer.yaml:

python scripts/train.py --log_dir instancerefer

Evaluation

You need specific the use_checkpoint with the folder that contains model.pth in config/InstanceRefer.yaml and run with:

python scripts/eval.py

Pre-trained Models

Input	ACC@0.25	ACC@0.5	Checkpoints
xyz+rgb	37.6	30.7	Baidu Netdisk [password: lrpb]

TODO

• Updating to the best version.
• Release codes for prediction on benchmark.
• Release pre-trained model.
• Merge PointGroup in an end-to-end manner.

Acknowledgement

This project is not possible without multiple great opensourced codebases.

• ScanRefer
• PointGroup
• torchsaprse
• pytorch_geometric

License

• This repository is released under MIT License (see LICENSE file for details).

InstanceRefer
├── data
│   ├── glove.p
│   ├── ScanRefer_filtered.json
│   ├── ...
│   ├── scannet
│   │  ├── meta_data
│   │  ├── pointgroup_data
│   │  │  ├── scene0000_00_aligned_bbox.npy
│   │  │  ├── scene0000_00_aligned_vert.npy
│   │  ├──├──  ... ...

cd data/scannet/
python prepare_data.py --split train --pointgroupinst_path [YOUR_PATH]
python prepare_data.py --split val   --pointgroupinst_path [YOUR_PATH]
python prepare_data.py --split test  --pointgroupinst_path [YOUR_PATH]

What is GEEK

Buddha Community

Tensorflex: Tensorflow Bindings for The Elixir Programming Language

Tensorflex

The paper detailing Tensorflex was presented at NeurIPS/NIPS 2018 as part of the MLOSS workshop. The paper can be found here. 

Contents

• How to run
• Documentation
• Examples
• Pull Requests Made

How to run

• You need to have the Tensorflow C API installed. Look here for details.
• You also need the C library libjpeg. If you are using Linux or OSX, it should already be present on your machine, otherwise be sure to install (brew install libjpeg for OSX, and sudo apt-get install libjpeg-dev for Ubuntu).
• Simply add Tensorflex to your list of dependencies in mix.exs and you are good to go!:

{:tensorflex, "~> 0.1.2"}

In case you want the latest development version use this:

{:tensorflex, github: "anshuman23/tensorflex"}

Documentation

Tensorflex contains three main structs which handle different datatypes. These are %Graph, %Matrix and %Tensor. %Graph type structs handle pre-trained graph models, %Matrix handles Tensorflex 2-D matrices, and %Tensor handles Tensorflow Tensor types. The official Tensorflow documentation is present here and do note that this README only briefly discusses Tensorflex functionalities.

read_graph/1:

Used for loading a Tensorflow .pb graph model in Tensorflex.

Reads in a pre-trained Tensorflow protobuf (.pb) Graph model binary file.

Returns a tuple {:ok, %Graph}.

%Graph is an internal Tensorflex struct which holds the name of the graph file and the binary definition data that is read in via the .pb file.

get_graph_ops/1:

Used for listing all the operations in a Tensorflow .pb graph.

Reads in a Tensorflex %Graph struct obtained from read_graph/1.

Returns a list of all the operation names (as strings) that populate the graph model.

create_matrix/3:

Creates a 2-D Tensorflex matrix from custom input specifications.

Takes three input arguments: number of rows in matrix (nrows), number of columns in matrix (ncols), and a list of lists of the data that will form the matrix (datalist).

Returns a %Matrix Tensorflex struct type.

matrix_pos/3:

Used for accessing an element of a Tensorflex matrix.

Takes in three input arguments: a Tensorflex %Matrix struct matrix, and the row (row) and column (col) values of the required element in the matrix. Both row and col here are NOT zero indexed.

Returns the value as float.

size_of_matrix/1:

Used for obtaining the size of a Tensorflex matrix.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {nrows, ncols} where nrows represents the number of rows of the matrix and ncols represents the number of columns of the matrix.

append_to_matrix/2:

Appends a single row to the back of a Tensorflex matrix.

Takes a Tensorflex %Matrix matrix as input and a single row of data (with the same number of columns as the original matrix) as a list of lists (datalist) to append to the original matrix.

Returns the extended and modified %Matrix struct matrix.

matrix_to_lists/1:

Converts a Tensorflex matrix (back) to a list of lists format.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a list of lists representing the data stored in the matrix.

NOTE: If the matrix contains very high dimensional data, typically obtained from a function like load_csv_as_matrix/2, then it is not recommended to convert the matrix back to a list of lists format due to a possibility of memory errors.

float64_tensor/2, float32_tensor/2, int32_tensor/2:

Creates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor from Tensorflex matrices containing the values and dimensions specified.

Takes two arguments: a %Matrix matrix (matrix1) containing the values the tensor should have and another %Matrix matrix (matrix2) containing the dimensions of the required tensor.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor/1, float32_tensor/1, int32_tensor/1, string_tensor/1:

Creates a TF_DOUBLE, TF_FLOAT, TF_INT32, or TF_STRING constant value one-dimensional tensor from the input value specified.

Takes in a float, int or string value (depending on function) as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor_alloc/1, float32_tensor_alloc/1, int32_tensor_alloc/1:

Allocates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor of specified dimensions.

This function is generally used to allocate output tensors that do not hold any value data yet, but will after the session is run for Inference. Output tensors of the required dimensions are allocated and then passed to the run_session/5 function to hold the output values generated as predictions.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the potential tensor data and type.

tensor_datatype/1:

Used to get the datatype of a created tensor.

Takes in a %Tensor struct tensor as input.

Returns a tuple {:ok, datatype} where datatype is an atom representing the list of Tensorflow TF_DataType tensor datatypes. Click here to view a list of all possible datatypes.

load_image_as_tensor/1:

Loads JPEG images into Tensorflex directly as a TF_UINT8 tensor of dimensions image height x image width x number of color channels.

This function is very useful if you wish to do image classification using Convolutional Neural Networks, or other Deep Learning Models. One of the most widely adopted and robust image classification models is the Inception model by Google. It makes classifications on images from over a 1000 classes with highly accurate results. The load_image_as_tensor/1 function is an essential component for the prediction pipeline of the Inception model (and for other similar image classification models) to work in Tensorflex.

Reads in the path to a JPEG image file (.jpg or .jpeg).

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the tensor data and type. Here the created Tensor is a uint8 tensor (TF_UINT8).

NOTE: For now, only 3 channel RGB JPEG color images can be passed as arguments. Support for grayscale images and other image formats such as PNG will be added in the future.

loads_csv_as_matrix/2:

Loads high-dimensional data from a CSV file as a Tensorflex 2-D matrix in a super-fast manner.

The load_csv_as_matrix/2 function is very fast-- when compared with the Python based pandas library for data science and analysis' function read_csv on the test.csv file from MNIST Kaggle data (source), the following execution times were obtained:

• read_csv: 2.549233 seconds
• load_csv_as_matrix/2: 1.711494 seconds

This function takes in 2 arguments: a path to a valid CSV file (filepath) and other optional arguments opts. These include whether or not a header needs to be discarded in the CSV, and what the delimiter type is. These are specified by passing in an atom :true or :false to the header: key, and setting a string value for the delimiter: key. By default, the header is considered to be present (:true) and the delimiter is set to ,.

Returns a %Matrix Tensorflex struct type.

run_session/5:

Runs a Tensorflow session to generate predictions for a given graph, input data, and required input/output operations.

This function is the final step of the Inference (prediction) pipeline and generates output for a given set of input data, a pre-trained graph model, and the specified input and output operations of the graph.

Takes in five arguments: a pre-trained Tensorflow graph .pb model read in from the read_graph/1 function (graph), an input tensor with the dimensions and data required for the input operation of the graph to run (tensor1), an output tensor allocated with the right dimensions (tensor2), the name of the input operation of the graph that needs where the input data is fed (input_opname), and the output operation name in the graph where the outputs are obtained (output_opname). The input tensor is generally created from the matrices manually or using the load_csv_as_matrix/2 function, and then passed through to one of the tensor creation functions. For image classification the load_image_as_tensor/1 can also be used to create the input tensor from an image. The output tensor is created using the tensor allocation functions (generally containing alloc at the end of the function name).

Returns a List of Lists (similar to the matrix_to_lists/1 function) containing the generated predictions as per the output tensor dimensions.

add_scalar_to_matrix/2:

Adds scalar value to matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

subtract_scalar_from_matrix/2:

Subtracts scalar value from matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

multiply_matrix_with_scalar/2:

Multiplies scalar value with matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

divide_matrix_by_scalar/2:

Divides matrix values by scalar.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.

add_matrices/2:

Adds two matrices of same dimensions together.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

subtract_matrices/2:

Subtracts matrix2 from matrix1.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.

tensor_to_matrix/1:

Converts the data stored in a 2-D tensor back to a 2-D matrix.

Takes in a single argument as a %Tensor tensor (any TF_Datatype).

Returns a %Matrix 2-D matrix.

NOTE: Tensorflex doesn't currently support 3-D matrices, and therefore tensors that are 3-D (such as created using the load_image_as_tensor/1 function) cannot be converted back to a matrix, yet. Support for 3-D matrices will be added soon.

Examples

Examples are generally added in full description on my blog here. A blog post covering how to do classification on the Iris Dataset is present here.

---

INCEPTION CNN MODEL EXAMPLE:

Here we will briefly touch upon how to use the Google V3 Inception pre-trained graph model to do image classficiation from over a 1000 classes. First, the Inception V3 model can be downloaded here: http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz

After unzipping, see that it contains the graphdef .pb file (classify_image_graphdef.pb) which contains our graph definition, a test jpeg image that should identify/classify as a panda (cropped_panda.pb) and a few other files I will detail later.

Now for running this in Tensorflex first the graph is loaded:

iex(1)> {:ok, graph} = Tensorflex.read_graph("classify_image_graph_def.pb")
2018-07-29 00:48:19.849870: W tensorflow/core/framework/op_def_util.cc:346] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
{:ok,
 %Tensorflex.Graph{
   def: #Reference<0.2597534446.2498625538.211058>,
   name: "classify_image_graph_def.pb"
 }}

Then the cropped_panda image is loaded using the new load_image_as_tensor function:

iex(2)> {:ok, input_tensor} = Tensorflex.load_image_as_tensor("cropped_panda.jpg")
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_uint8,
   tensor: #Reference<0.2597534446.2498625538.211093>
 }}

Then create the output tensor which will hold out output vector values. For the inception model, the output is received as a 1008x1 tensor, as there are 1008 classes in the model:

iex(3)> out_dims = Tensorflex.create_matrix(1,2,[[1008,1]])
%Tensorflex.Matrix{
  data: #Reference<0.2597534446.2498625538.211103>,
  ncols: 2,
  nrows: 1
}

iex(4)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(out_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.2597534446.2498625538.211116>
 }}

Then the output results are read into a list called results. Also, the input operation in the Inception model is DecodeJpeg and the output operation is softmax:

iex(5)> results = Tensorflex.run_session(graph, input_tensor, output_tensor, "DecodeJpeg", "softmax")
2018-07-29 00:51:13.631154: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
[
  [1.059142014128156e-4, 2.8240500250831246e-4, 8.30648496048525e-5,
   1.2982363114133477e-4, 7.32232874725014e-5, 8.014426566660404e-5,
   6.63459359202534e-5, 0.003170756157487631, 7.931600703159347e-5,
   3.707312498590909e-5, 3.0997329304227605e-5, 1.4232713147066534e-4,
   1.0381334868725389e-4, 1.1057958181481808e-4, 1.4321311027742922e-4,
   1.203602587338537e-4, 1.3130248407833278e-4, 5.850398520124145e-5,
   2.641105093061924e-4, 3.1629020668333396e-5, 3.906813799403608e-5,
   2.8646905775531195e-5, 2.2863158665131778e-4, 1.2222197256051004e-4,
   5.956588938715868e-5, 5.421260357252322e-5, 5.996063555357978e-5,
   4.867801326327026e-4, 1.1005574924638495e-4, 2.3433618480339646e-4,
   1.3062104699201882e-4, 1.317620772169903e-4, 9.388553007738665e-5,
   7.076268957462162e-5, 4.281177825760096e-5, 1.6863139171618968e-4,
   9.093972039408982e-5, 2.611844101920724e-4, 2.7584232157096267e-4,
   5.157176201464608e-5, 2.144951868103817e-4, 1.3628098531626165e-4,
   8.007588621694595e-5, 1.7929042223840952e-4, 2.2831936075817794e-4,
   6.216531619429588e-5, 3.736453436431475e-5, 6.782123091397807e-5,
   1.1538144462974742e-4, ...]
]

Finally, we need to find which class has the maximum probability and identify it's label. Since results is a List of Lists, it's better to read in the nested list. Then we need to find the index of the element in the new list which as the maximum value. Therefore:

iex(6)> max_prob = List.flatten(results) |> Enum.max
0.8849328756332397

iex(7)> Enum.find_index(results |> List.flatten, fn(x) -> x == max_prob end)
169

We can thus see that the class with the maximum probability predicted (0.8849328756332397) for the image is 169. We will now find what the 169 label corresponds to. For this we can look back into the unzipped Inception folder, where there is a file called imagenet_2012_challenge_label_map_proto.pbtxt. On opening this file, we can find the string class identifier for the 169 class index. This is n02510455 and is present on Line 1556 in the file. Finally, we need to match this string identifier to a set of identification labels by referring to the file imagenet_synset_to_human_label_map.txt file. Here we can see that corresponding to the string class n02510455 the human labels are giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (Line 3691 in the file).

Thus, we have correctly identified the animal in the image as a panda using Tensorflex!

---

RNN LSTM SENTIMENT ANALYSIS MODEL EXAMPLE:

A brief idea of what this example entails:

• The Recurrent Neural Network utilizes Long-Short-Term-Memory (LSTM) cells for holding the state for the data flowing in through the network
• In this example, we utilize the LSTM network for sentiment analysis on movie reviews data in Tensorflex. The trained models are originally created as part of an online tutorial (source) and are present in a Github repository here.

To do sentiment analysis in Tensorflex however, we first need to do some preprocessing and prepare the graph model (.pb) as done multiple times before in other examples. For that, in the examples/rnn-lstm-example directory there are two scripts: freeze.py and create_input_data.py. Prior to explaining the working of these scripts you first need to download the original saved models as well as the datasets:

• For the model, download from here and then store all the 4 model files in the examples/rnn-lstm-example/model folder
• For the dataset, download from here. After decompressing, we do not need all the files, just the 2 numpy binaries wordsList.npy and wordVectors.npy. These will be used to encode our text data into UTF-8 encoding for feeding our RNN as input.

Now, for the Python two scripts: freeze.py and create_input_data.py:

• freeze.py: This is used to create our pb model from the Python saved checkpoints. Here we will use the downloaded Python checkpoints' model to create the .pb graph. Just running python freeze.py after putting the model files in the correct directory will do the trick. In the same ./model/ folder, you will now see a file called frozen_model_lstm.pb. This is the file which we will load into Tensorflex. In case for some reason you want to skip this step and just get the loaded graph here is a Dropbox link
• create_input_data.py: Even if we can load our model into Tensorflex, we also need some data to do inference on. For that, we will write our own example sentences and convert them (read encode) to a numeral (int32) format that can be used by the network as input. For that, you can inspect the code in the script to get an understanding of what is happening. Basically, the neural network takes in an input of a 24x250 int32 (matrix) tensor created from text which has been encoded as UTF-8. Again, running python create_input_data.py will give you two csv files (one indicating positive sentiment and the other a negative sentiment) which we will later load into Tensorflex. The two sentences converted are:
  • Negative sentiment sentence: That movie was terrible.
  • Positive sentiment sentence: That movie was the best one I have ever seen.

Both of these get converted to two files inputMatrixPositive.csv and inputMatrixNegative.csv (by create_input_data.py) which we load into Tensorflex next.

Inference in Tensorflex: Now we do sentiment analysis in Tensorflex. A few things to note:

• The input graph operation is named Placeholder_1
• The output graph operation is named add and is the eventual result of a matrix multiplication. Of this obtained result we only need the first row
• Here the input is going to be a integer valued matrix tensor of dimensions 24x250 representing our sentence/review
• The output will have 2 columns, as there are 2 classes-- for positive and negative sentiment respectively. Since we will only be needing only the first row we will get our result in a 1x2 vector. If the value of the first column is higher than the second column, then the network indicates a positive sentiment otherwise a negative sentiment. All this can be observed in the original repository in a Jupyter notebook here: ```elixir iex(1)> {:ok, graph} = Tensorflex.read_graph "examples/rnn-lstm-example/model/frozen_model_lstm.pb" {:ok, %Tensorflex.Graph{ def: #Reference<0.713975820.1050542081.11558>, name: "examples/rnn-lstm-example/model/frozen_model_lstm.pb" }}

iex(2)> Tensorflex.get_graph_ops graph ["Placeholder_1", "embedding_lookup/params_0", "embedding_lookup", "transpose/perm", "transpose", "rnn/Shape", "rnn/strided_slice/stack", "rnn/strided_slice/stack_1", "rnn/strided_slice/stack_2", "rnn/strided_slice", "rnn/stack/1", "rnn/stack", "rnn/zeros/Const", "rnn/zeros", "rnn/stack_1/1", "rnn/stack_1", "rnn/zeros_1/Const", "rnn/zeros_1", "rnn/Shape_1", "rnn/strided_slice_2/stack", "rnn/strided_slice_2/stack_1", "rnn/strided_slice_2/stack_2", "rnn/strided_slice_2", "rnn/time", "rnn/TensorArray", "rnn/TensorArray_1", "rnn/TensorArrayUnstack/Shape", "rnn/TensorArrayUnstack/strided_slice/stack", "rnn/TensorArrayUnstack/strided_slice/stack_1", "rnn/TensorArrayUnstack/strided_slice/stack_2", "rnn/TensorArrayUnstack/strided_slice", "rnn/TensorArrayUnstack/range/start", "rnn/TensorArrayUnstack/range/delta", "rnn/TensorArrayUnstack/range", "rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3", "rnn/while/Enter", "rnn/while/Enter_1", "rnn/while/Enter_2", "rnn/while/Enter_3", "rnn/while/Merge", "rnn/while/Merge_1", "rnn/while/Merge_2", "rnn/while/Merge_3", "rnn/while/Less/Enter", "rnn/while/Less", "rnn/while/LoopCond", "rnn/while/Switch", "rnn/while/Switch_1", "rnn/while/Switch_2", "rnn/while/Switch_3", ...]

First we will try for positive sentiment:
```elixir
iex(3)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixPositive.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13138>,
  ncols: 250,
  nrows: 24
}

iex(4)> input_dims = Tensorflex.create_matrix(1,2,[[24,250]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.13575>,
  ncols: 2,
  nrows: 1
}

iex(5)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals, input_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.14434>
 }}

iex(6)> output_dims = Tensorflex.create_matrix(1,2,[[24,2]])
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.14870>,
  ncols: 2,
  nrows: 1
}

iex(7)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(output_dims)
{:ok,
 %Tensorflex.Tensor{
   datatype: :tf_float,
   tensor: #Reference<0.713975820.1050542081.15363>
 }}

We only need the first row, the rest do not indicate anything:

iex(8)> [result_pos | _ ] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [4.483788013458252, -1.273943305015564],
  [-0.17151066660881042, -2.165886402130127],
  [0.9569928646087646, -1.131581425666809],
  [0.5669126510620117, -1.3842089176177979],
  [-1.4346938133239746, -4.0750861167907715],
  [0.4680981934070587, -1.3494354486465454],
  [1.068990707397461, -2.0195648670196533],
  [3.427264451980591, 0.48857203125953674],
  [0.6307879686355591, -2.069119691848755],
  [0.35061028599739075, -1.700657844543457],
  [3.7612719535827637, 2.421398878097534],
  [2.7635951042175293, -0.7214710116386414],
  [1.146680235862732, -0.8688814640045166],
  [0.8996094465255737, -1.0183486938476563],
  [0.23605018854141235, -1.893072247505188],
  [2.8790698051452637, -0.37355837225914],
  [-1.7325369119644165, -3.6470277309417725],
  [-1.687785029411316, -4.903762340545654],
  [3.6726789474487305, 0.14170047640800476],
  [0.982108473777771, -1.554244875907898],
  [2.248904228210449, 1.0617655515670776],
  [0.3663095533847809, -3.5266385078430176],
  [-1.009346604347229, -2.901120901107788],
  [3.0659966468811035, -1.7605335712432861]
]

iex(9)> result_pos
[4.483788013458252, -1.273943305015564]

Thus we can clearly see that the RNN predicts a positive sentiment. For a negative sentiment, next:

iex(10)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixNegative.csv", header: :false)
%Tensorflex.Matrix{
  data: #Reference<0.713975820.1050542081.16780>,
  ncols: 250,
  nrows: 24
}

iex(11)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals,input_dims)
{:ok,              
 %Tensorflex.Tensor{
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.16788>
 }}

iex(12)> [result_neg|_] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
[
  [0.7635725736618042, 10.895986557006836],
  [2.205151319503784, -0.6267685294151306],
  [3.5995595455169678, -0.1240251287817955],
  [-1.6063352823257446, -3.586883068084717],
  [1.9608432054519653, -3.084211826324463],
  [3.772461414337158, -0.19421455264091492],
  [3.9185996055603027, 0.4442034661769867],
  [3.010765552520752, -1.4757057428359985],
  [3.23650860786438, -0.008513949811458588],
  [2.263028144836426, -0.7358709573745728],
  [0.206748828291893, -2.1945853233337402],
  [2.913491725921631, 0.8632720708847046],
  [0.15935257077217102, -2.9757845401763916],
  [-0.7757357358932495, -2.360766649246216],
  [3.7359719276428223, -0.7668198347091675],
  [2.2896337509155273, -0.45704856514930725],
  [-1.5497230291366577, -4.42919921875],
  [-2.8478822708129883, -5.541027545928955],
  [1.894787073135376, -0.8441318273544312],
  [0.15720489621162415, -2.699129819869995],
  [-0.18114641308784485, -2.988100051879883],
  [3.342879056930542, 2.1714375019073486],
  [2.906526565551758, 0.18969044089317322],
  [0.8568912744522095, -1.7559258937835693]
]
iex(13)> result_neg
[0.7635725736618042, 10.895986557006836]

Thus we can clearly see that in this case the RNN indicates negative sentiment! Our model works!

Pull Requests Made

• In chronological order:
  • PR #2: Renamed app to Tensorflex from TensorflEx
  • PR #3: Added support for reading pretrained graph definition files
  • PR #4: Merged all support functions into read_graph; returning atoms
  • PR #6: Returning list of op names in get_graph_ops and extended error atoms to all TF error codes
  • PR #7: Added tensor support for strings and for getting TF_DataType
  • PR #8: Added matrix functions, numeral tensors, better returns & removed unnecessary POC code
  • PR #9: Added freeze graph Python example
  • PR #10: Tensors of TF_FLOAT and TF_DOUBLE type both supported
  • PR #11: Tensor allocations and Tensorflow Session support added
  • PR #12: Fixed issue regarding printing of outputs
  • PR #13: Added another example for blog post
  • PR #14: Added graph file check before loading
  • PR #15: Added tests
  • PR #16: Add linker flags for MacOS
  • PR #18: Added image loading capabilities (as 3D uint8 Tensors)
  • PR #19: Wrapped references around structs
  • PR #20: Moved basic C error checking to Elixir
  • PR #21: Added Keras example
  • PR #22: Added "append" function for matrices
  • PR #23: Added fast C based direct CSV-to-matrix functionality with options
  • PR #24: Added TF_INT32 tensors and tensor allocators
  • PR #25: Added RNN (LSTM) example
  • PR #26: Added documentation
  • PR #28: Added improved tests
  • PR #29: Adding metadata to mix.exs
  • PR #31: Update nifs.ex
  • PR #32: Fixed indentation and corrected warnings
  • PR #35: Added new matrix operations
  • PR #36: Fixed bugs in C code
  • PR #37: Added tensor_to_matrix/1 (with tests/docs)
  • PR #38: Create CONTRIBUTING.md
  • PR #39: Formatted C code

---

Author: anshuman23
Source code: https://github.com/anshuman23/tensorflex
License: Apache-2.0 license

#elixir #tensorflow #machine-learning 

Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

• Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

• Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

• Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market

Top 10 Multi vendor Marketplace Platform Providers 2022

Great evolution has happened in the buying and selling process due to the advent of ecommerce. There is exponential growth in the field of online business and selling and buying happens at the doorstep. The multi seller ecommerce platform has become the next level in the ecommerce niche.

The ecommerce marketplace platforms like Amazon, Flipkart, and eBay have already succeeded in the industry and have set a milestone on sales and revenue.

This fact has inspired many aspiring entrepreneurs and has made them transfer their brick and mortar stores to multi vendor platform.

What is Online Multi vendor Marketplace?

Multi vendor marketplace platform is connect a multiple sellere or vendors to display and sell their products through the platform by agreeing with the terms mentioned by the admin of the platform. They can have their way of promoting their products.

Here is a list of Top 10 Best Turnkey Multi vendor Marketplace Platforms:

Now that you have a better understanding of the key features for Multi vendor marketplace, let’s compare ten of the top Multi vendor providers.

1. Zielcommerce – All in One Multi vendor eCommerce Marketplace Platform

Visit Website

Zielcommerce is a white label enabled enterprise grade online marketplace software. The multi vendor platform comes with a one time payment option and it is completely customizable and also scalable. 

Platform Highlights

• The feature-rich UI and UX have never missed attracting the users towards the multi vendor ecommerce.
• The platform is user-friendly and it is a perfect device compatible.
• It’s a convenient marketplace solution that provides all round service required for a perfect multi vendor marketplace platform.
• This also supports easy brand recognition and you can get more visitors to your ecommerce platform.

Zielcommerce provides its users with a secured environment through its SSL certified marketplace software and gains the trust of the users. You can be easily promoted online with this SEO-optimized platform. Stay connected with your customers all the time with the in-build communication channels.

The pleasing features of this multi vendor marketplacce solution

• Multilingual and multiple currency support
• Multiple payment options to facilitate buyers
• Real-time tracking feature to track orders online
• Wide delivery option for buyers’ convenience
• A dedicated mobile application that will suit your business demands
• Review and rating system to enhance the performance of the platform
• 24/7 technical support from our end.

Best Use Cases

• Grocery Ecommerce Platform
• Electronics Ecommerce Platform
• Furniture Ecommerce Platform
• Handcraft Ecommerce Software
• Health Care & Pharmacy Platform

Client’s Rating

• Ease of use: 4.5/5
• Customer service – 4.7/5
• Overall: 4.6/5

Explore Zielcommerce Multi vendor Ecommerce Platform

2. X-cart – a well-known multi vendor marketplace platform solution

X-cart is a standalone online marketplace solution for your online business needs. You can get the complete comprehensive features within this multi vendor marketplace software that can meet the customers’ expectations. A genuine approach is maintained and the users trust X-cart for its outstanding functionalities that satisfy the multi vendor market demands.

Platform Highlights

• Xcart stands alone in the market by providing best multi vendor marketplace solutions that will facilitate users to increase their online credibility.
• If you prefer to build a multi vendor marketplace platform like amazon or eBay then Xcart is the perfect choice.

It has gained the trust of thousands of users and people who use Xcart as their online multi vendor marketplace software has given the best review about the product.

The salient features of this multi vendor ecommerce platform solution

• One-time payment to purchase the marketplace software
• In-built marketing and promotion tools to promote the multi vendor marketplace software
• Trusted payment gateways integrated with the website
• Reliable order management system.
• On-time delivery management

Best Use Cases

• Hyperlocal Ecommerce Platform
• Jewellery e-commerce Store
• Electronics Ecommerce Platform
• Furniture Ecommerce Platform

Client’s Rating:

• Ease of use : 3.5/5
• Customer service – 3.4/5
• Overall : 3.5/5

Explore Xcart Multi vendor Marketplace Software

3. Cs cart – a perfect multi vendor marketplace solution

CS-Cart has never disappointed its users and it comes with the complete ecommerce marketplace solution for all your business demands. You can gain perfect control over the online multi vendor marketplace platform and can personalize the platform to suit your business needs. You will get higher visibility and can easily attract your target audience with the CS-Cart marketplace solution.

Platform Highlights

• CS-cart is the most reliable best ecommerce marketplace platform that gives a user-friendly platform for the user.
• The interface is easily understandable and no technical knowledge is needed to maintain the multi vendor marketplace software.

You can gain the attention of global audiences through its multilingual support and can take your brand all over the world and build a strong branding with the help of CS cart.

The key features of this multi vendor ecommerce website solution

• SEO- friendly platform that will help you to get top ranking in all search engines.
• Mobile –friendly and will get you more mobile users as your customers
• Get genuine customer care support
• Well-integrated with all third-party software.
• The online multi vendor ecommerce platform will have social media logins

Best use cases

• Fashion and designs marketplace platform
• Book selling ecommerce platform
• Service marketplace platform
• Food ordering multivendor ecommerce platform

Client’s Rating :

• Ease of use : 4.1/5
• Customer service – 4.3/5
• Overall :4.2/5

[Explore Cscart Online Marketplace Software](https://www.cs-cart.com “Explore Cscart Online Marketplace Software”)

4. Arcadier – Superlative Multi vendor marketplace platform

Arcadier is the SaaS (Software-as-a-Service) provider that allows businesses, SMEs, local communities, government agencies and entrepreneurs to manage their online multi vendor marketplace platform more efficiently and affectionately. Arcadier has many attractive features that can grab the attention of vendors.

Platform Highlights

• When you have multiple vendors only then you can call your platform as a best ecommerce marketplace platform.
• This is quite easy when you go for Arcadier.
• The genuine support that you get with this online multi vendor enterprise marketplace platform will retain your vendors and also your buyers and provides better multi vendor marketplace software to your business needs.

Apart from other SaaS online marketplace platforms on the market that offer a temporary solution for all purposes, Arcadier allows users to choose between multiple options in buying and selling products or services to rental spaces and other business models.

**The Prominent features of this online multi vendor marketplace software solution

• The seller can manage to add variations to each listing, and also placing images and surcharges on each and every variant.
• User-friendly platform to get the top ranking in all search engines.
• Manually configure specific dates and hours of your ads on the calendar.
• A Complete customer support assistance

Best Use Case

• Clothing and accessories multi vendor ecommerce platform
• Handicrafts Online marketplace platform
• Hair stylist scheduling software
• On demand movies online marketplace platform

**Client’s Rating: **

• Ease of use : 3.2/5
• Customer service – 3/5
• Overall : 3.1/5

Explore Arcadier Multi vendor Marketplace Platform

5. Bigcommerce - Exquisite Multi Vendor Marketplace Software

Multi vendor Marketplace that converts your single admin online store into Multi vendor Marketplace. It provides of adding vendors and maintain the track record of their order and sales. Apart from vendor features, Bigcommerce gives best buyer features that will impress buyers and make them decide on buying products in your online multi vendor ecommerce platform.

Platform Highlights

• Offers and discount features are available that will delight buyers and will make them refer more customers to your best ecommerce marketplace platform.
• You can also easily retain your customers by keeping them about new arrivals and offers.
• As a store admin, you have background access and control and manage the products, orders, vendors and their products.

It comes with an option which, without the approval of the vendor admin the product would not be visible in the forefront. This online multi vendor marketplace platform is excellent features and creating various plans for vendors, a payment management system for vendors.

Impressing features of this online multi vendor marketplace software solution

• Flexible Functionality Product approval
• An admin can have full access to the seller’s profile, products, manage
• Synchronizing products and orders from the “Bigcommerce store” to the "market.
• Without any issue, the admin can create a “Payment” for the seller, once a product is out of stock.

Best Use Case

• best online salon scheduling software for salon owners
• Educational books online marketplace platform
• Food delivery multi seller ecommerce platform
• Fashion and clothing ecommerce platform

Explore Bigcommerce Multi vendor Ecommerce Platform

6. IXXO - Ideal Multi Vendor marketplace software

Ixxo is an ideal marketplace solution for those who want to open and manage a high-volume marketplace as IXXO online Multi Vendor ecommerce platform offers unlimited product and unlimited vendor capacity. The marketplace owners can configure vendor privileges purely based on vendors. this help the multi vendor marketplace software owner to provide the basic vendor features, where the vendors dont have much ecommerce experience and privileges.

Platform Highlights

• IXXO is a well-known online multi vendor marketplace platform that ensures perfect product delivery on-time.
• Buyers and vendors can easily track their product and will know its current status.

This will ensure that the delivery is taking place in the right way. If there is any delay then through a proper messaging system the buyer will get intimation regarding the delay. This feature impresses the customer and makes the platform the best one.

Splendid features of this best ecommerce marketplace platform solution

• Simple Checkout Process
• A wide range of payment options
• Responds promptly and friendlily.
• Feature-rich provider dashboard.

Best Use Cases

• Ticket booking multi vendor ecommerce platform
• On-demand cab booking online marketplace platform
• Online grocery ecommerce platform
• Clothing ecommerce platform

**Client’s Rating: **

• Ease of use : 4.1/5
• Customer service – 4.3/5
• Overall :4.2/5

Explore IXXO cart Online best marketplace softwarebest marketplace software

7. Sharetribe - Structured Multi vendor marketplace solutions

Sharetribe is one of the excellent SaaS platforms for building and launching a online multi vendor marketplace software. Easy setting changes to your color theme and photos, instantly.

Platform Highlights

• It is merged with a integration process, the marketplace allows users to sell products or services online without any technical support.
• Sharetribe has all in-built marketing tools that will easily promote your brand globally with less effort.

This online multi vendor marketplace platform gives a perfect shopping experience to customers and also satisfied selling experience to vendors. Users can trust sharetribe for their business requirement and can get a trustworthy marketplace solution that will leverage their business to greater levels.

Core features of this best ecommerce marketplace platform

• It is a comprehensive tool for customizing your marketplace.
• Responsive Design for users, optimized for every screen.
• More conversions rate and decrease in bounce rate.
• A comprehensive content management system to maintain an active market with visual content.

Best Use Cases

• Travel and stay multi vendor ecommerce platform
• salon appointment scheduling software
• Healthcare ecommerce platform
• Food supply ecommerce platform

**Client’s Rating:**

• Ease of use : 3.9/5
• Customer service – 3.7/5
• Overall :3.8/5

Explore Sharetribe Multi vendor Marketplace Software

8. Appdupe - Intuitive Multi vendor marketplace software

A online Multi vendor marketplace platform is an online marketplace where many sellers can sign up, create their profiles and add products and sell when they want. One of the best examples of multi vendor platforms right now is Amazon, and so on. Well, the ecommerce marketplace software has multiple benefits for its users and vendors.

Platform Highlights

• Appdupe gives customers to share their reviews and give ratings for the product they have purchased.
• Vendors can also read the reviews written by their customers and this will help them to enhance their online multi vendor marketplace platform in a better way.
• Appdupe also supports multiple revenue models and users can select the one that perfectly suits their business and can get better returns with minimum investment.

Impressing feature of this best ecommerce marketplace platform

• It provides a hassle-free process
• Easily download and handle their products in a simple way.
• Separate dashboard for seller and buyer data formatting may go all the way.
• Analysis and enhanced the ROI

Best Use Cases

• Hyperlocal multivendor ecommerce platform
• Grocery ecommerce platform
• Mobile & accessories online marketplace platform
  * Cosmetics ecommerce platform
  
  Explore Appdupe ecommerce marketplace software

9. Miva - Outbreaking Multi vendor marketplace platform

Is a flexible multi seller ecommerce platform that can be easily modified as their business evolves with more conversions rate, better integrations, with complete solutions for all aspects of online sales, This online multi vendor marketplace software help them generate revenue and increasing the average order value and with less operating costs.

Platform Highlights

Miva suits to any business model and business size. This online multi vendor marketplace platform is very cost-effective and even a startup who plans to start an online store with minimum investment can easily go for Miva.

The online ecommerce marketplace software looks like it has been built from scratch. It inherits all essential features that are needed to run a multi vendor marketplace platform successfully. All you need is to buy the platform and launch the marketplace and can start earning instantly.

Intuitive feature of this best marketplace software

• Admin can control and manage the review and approval of new products
• Flexible commissions for every vendor sale based on subscription plans
• Separate dashboard for a vendor to manage their own product listing
• The separate seller has a unique profile on the marketplace and products limits based on membership plans

Best Use Cases

• Travel ticket booking online multi vendor ecommerce platform
• Grocery and diary online marketplace platform
• Electrical and electronics ecommerce platform
• Healthcare multi vendor ecommerce platform.

**Client’s Rating: **

• Ease of use:4/5
• Customer service – 3.7/5
• Overall: 3.9/5

10. Quick eSelling – A Proven Multi vendor Marketpalce platform

Quick eSelling is a popular multi vendor online marketplace platform with upgrade features and a more comfortable platform for global merchants and seller to start their own online store. Quick eSelling is an online store feature for Customer Engagement and Retention. This platform has been designed to help you significantly increase your sales and save time.

Platform Highlights

• Quick e-selling marketplace platform allows you to set commission plan for every individual vendor.
• You can easily analyze their performance through proper analytics and reports.
• You can boost the poor performing vendor by providing less commission percentage and boost their sale.

his will satisfy vendors and will make them stay with your best multi vendor marketplace software for a long time. You can get complete support from the technical team round the clock. Whenever customization needed the technical team will guide you in designing your own online multi seller ecommerce platform.

The essential feature of this Multi vendor marketplace software

• SEO-friendly for ecommerce web development and essential to ensure high traffic
• Vendors can check sales trends through graphs and data information inputs for building strategies
• A secured platform for merchants and customers’ transitional communication.
• It makes it easy for you to launch your online business effectively

Best Use Cases

• Fashion and accessories marketplace platform
• E-Book marketplace software
• Food and beverages ecommerce platform
• Jewellery online multi vendor ecommerce platform.

Client’s Rating:

• Ease of use : 3.5/5
• Customerservice – 3.3/5
• Overall :3.4/5

11. Smartstore Z – a multifaceted marketplace platform

This is the most recommended online marketplace platform that holds thousands of active users. The advanced security feature supports users to store customers’ data in a secured way. The platform can stand against all malware attacks as it contains SSL certification.

Platform highlights

The extraordinary inventory management system will let sellers maintain their stock in an effective way. The order process will never be interrupted due to a shortage of stock. Proper notifications will be sent to respective sellers whenever their stock hits the minimum value.

This multi vendor software supports thousands of templates and plugins that can be used by users to customize their marketplace to meet their business demands. Being a user-friendly marketplace platform, no technical knowledge is needed to manage the platform. The dedicated dashboard will support the admin to know the exact working condition of their business.

Features of this multi vendor ecommerce marketplace platform.

• The platform is perfectly scalable and supports future business expansion and can hold a huge customer database for a long time.
• Buyers will be benefited as they can utilize multiple delivery options. They can select their convenient delivery slot and can order products.
• Users can integrate their existing software that is used for their business operations with this multi vendor ecommerce platform as it supports any third-party API.
• Sellers are allowed to have unlimited product listings that will help them to reach their customers easily.

 Best use cases

• On-demand cab booking marketplace platform
• Online ticket booking platform
• Refurbished good selling platform
• Hyperlocal multi vendor eCommerce platform

Client’s Rating: 

• Ease of use: 3.8/5
• Customer service – 3.4/5
• Overall: 3.6/5

Smartstore Z is the most compatible multi vendor eCommerce store that will fit into any business model and business size.

5.     Brainview – a commendable online marketplace platform

Brainview has the most enchanting UI & UX that can gain the attention of your target audience and will make them buy products on your platform. Users can get globally connected as the marketplace supports multiple languages and multiple currencies. Advanced technologies are implemented just to provide a seamless shopping experience to customers.

Platform highlights

Excellent customer support is offered by this multi vendor platform and customers can have direct communication with the concerned seller and they can get more details about the product or service before they buy. This will reduce returns and refunds. Many customer-attracting features are available in this marketplace platform like loyalty programs, referral programs, and many more.

Acquiring more customers is not at all a challenge for this best multi vendor marketplace software. Several revenue streams like commission fee, subscription fee, affiliate modules, advertisements, and many more are integrated with this online marketplace platform.

Features of this multi vendor eCommerce platform

• Supports a better authentication process that will avoid spam users entering the marketplace.
• A dedicated mobile application is available and users of both Android and iOS can use and get a better user experience.
• The platform offers multiple payment options that will facilitate customers to pay and buy online through secured payment gateways.
• Customers can enjoy offers and discounts for all products they buy through this robust multi-vendor platform and this will motivate them to refer others to your marketplace.
• Supports social media login and sharing as it benefits both sellers and buyers to login through social media credentials and share products in their social media pages.

Best use cases

• Grocery eCommerce marketplace platform
• Cosmetics and fashion accessories e-stores
• Online movies download eCommerce platform
• Hyperlocal multi-vendor eCommerce platform
• Online jewelry marketplace platform

Client’s Rating: 

·       Ease of use: 4/5

·       Customer service – 4.2/5

·       Overall: 4.1/5

Brainview is the significant multi vendor eCommerce store that give 100% customization and scalability to users.

Types of Multi vendor Marketplace Platform

There are several types of multi vendor marketplace software in the market. One needs to understand all the types and should know to identify which type of marketplace platform suits his business well.

• Vertical marketplace – this type of best marketplace software concentrates only on one particular service and you cannot find a wide range of services in these platforms. Etsy is a good example of a vertical marketplace where the platform sells handmade crafts alone.
• Horizontal marketplace – this platform is opposite to a vertical marketplace where you can find several types of services under one roof. Amazon is a perfect example of this type of marketplace.
• Product-based marketplace – you can find a wide range of products in this marketplace. The products can be physical goods or even digital goods. Amazon and Flipkart are the product-based marketplaces.
• Service-based marketplace – service providers will list their services like plumbing, personal care, pest control, and many more. Upwork and Fiverr are service-based marketplaces.

Start a Ecommerce Business with the Best Marketplace Platforms Provider

The million-dollar question that has arisen in the minds of every budding entrepreneur is how to start a online multi vendor ecommerce platform. Full attention is needed while building a ecommerce marketplace software. It is not as simple as you think. Only through this multi vendor ecommerce platform, you are going to be recognized by the vendors and the buyers. This multi vendor platform is going to earn you money so it cannot have any flaws.

One way of building a online multi vendor marketplace software is to build it from scratch. First, you need to hire a reputed multi vendor ecommerce platform development company that has ample knowledge about this field. Then you need to explain to them about your requirements and expectations.

They will develop and will show you the demo. During the demo session, you can let them know your modifications and they will also clarify your doubts. At last, your multi seller ecommerce platform will be ready to launch and you can start promoting your multi vendor marketplace software.

The major fact to be noted is, when you build a online multi vendor ecommerce platform from scratch you need to wait for a long time and you need to spend more on the development. If you are okay with it then you can proceed. Else you have another option to go with.

Another option is buying ready made online multi vendor marketplace software that will have all the essential features that are required to run the platform successfully. The software will be tested and proved so there will not be any flaws. You can instantly launch the software after purchasing.

You can get an instant solution to building a multi vendor online marketplace software. This method is quite very cost-effective and it is highly advisable for the startups that are new to this field. You can also customize the software to suit your business needs.

Must have Features in a multi vendor marketplace Platform

The features that are built in the online multi vendor ecommerce platform will determine the user experience and will gain customer satisfaction. Now let us check out the comprehensive features that are too in a multi vendor ecommerce platform.

• Easy customization – the buying behaviors of the customers keep changing so the multi vendor ecommerce website should keep changing over a while. So customization is a default expectation in any multi vendor ecommerce platform.
• Advanced search and navigation tool – the best user-interface will provide easy navigation and will let the buyers find the product in a simple way.
• Payment gateways – the online multi vendor ecommerce website should have multiple payment gateways integrated with it. This will provide more convenience to the buyers
• Secured website – the online multi vendor ecommerce platform should have an SSL configuration and should provide a secured transaction to the buyers and the vendors.
• Multi-lingual and multi-currencies support – for reaching a global audience the online multi vendor ecommerce website should support multiple languages and currencies.
• Review and ratings – the buyers expect this feature to be in the online multi vendor ecommerce platform they purchase the product.
• Simple checkout – a complicated checkout process will make the buyers abandon the site. You need to have hassle-free checkout procedures in your online multi vendor ecommerce platform.

Revenue generation channels on a multi vendor marketplace Platform:

The main objective of building a online multi vendor ecommerce platform is to earn profit and generate more sales. This will be the ultimate motive for any entrepreneur. We need to know what are the revenue sources that a multi vendor marketplace software provides to the admin of the platform.

• Commission fee – this is a mutual agreement made between the admin and the vendor where the vendor agrees to pay a certain percentage of commission on all products he sells through the online best multi vendor marketplace software. The commission percentage can vary from vendor to vendor.
• Subscription fee – the admin can set a subscription fee and can make the vendors subscribe with the online multi vendor marketplace software and become a paid member of the platform. The membership needs to be renewed over some time.
• Listing fee – the vendors will be charged when they want their products to be listed on the marketplace solutions.
• Advertisement fee – you can allot some space in your online ecommerce marketplace software for advertisement alone and can allow third-party to post their ads in the allotted space and you can charge them accordingly.\

How Products and Services are delivered in a multi vendor marketplace?

The multi vendor ecommerce platform will follow a hassle-free shipping and delivery process. This is where you can gain the maximum trust of your buyers and will also help you retain your customers effectively.

• Once the order is placed by the buyer, the notification is sent to the concerned vendor from the multi vendor ecommerce platform.
• The vendor will check the availability of the product and will arrange for shipping and delivery. In some cases, the admin of the online multi vendor ecommerce platform will take care of shipping and delivery.
• The online multi vendor ecommerce platform will be integrated with shipping logistics and the logistic people will come and collect the product from the seller and will deliver it to the customers.
• If a service is provided instead of a product then the service provider will get the notification and he will send his technical person to do the service to the customer place.

How it's Beneficial

Online buyers need a one-stop solution to fulfill all their demands. Without searching for several sites to buy several products, users can just visit one online store and find a variety of products and it is quite a time saving process. A single platform that contains several vendors and their products is called a multi vendor ecommerce platform.

With the evolution of technology, multi vendor marketplace platforms have taken a new dimension that can easily predict customers’ buying behavior. The key pillars involved in any multi vendor marketplace are the admin, vendors and buyers. The platform will act as a bridge to connect all of them and facilitate users to be benefited. Let us analyze the benefits of a multi vendor ecommerce platform in detail.

Conclusion

By all means, building best multi vendor marketplace software will surely benefit us and will let to get more returns. We should also accept the fact that there are many challenges that a online marketplace software will face at its early stage. But still the future of this industry is unbeatable and you can firmly set your mind in starting your own multi seller ecommerce platform.

Understanding the importance and the functioning of a online multi vendor marketplace software will help you to build a flawless multi vendor ecommerce software. When you build a multi vendor marketplace platform with utmost perfection then you can easily win the market and can gain your audience’s attention with less effort.

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Multivendor marketplace FREE Download Extension Module, Affiliate

Multi Vendor marketplace FREE Download Extension Module, Affiliate marketing Software : Multivendor marketplace is an e-commerce platform that allows multiple vendors to sell their products from one storefront. Multi-vendor marketplace provides a larger base of ready-to-buy customers to sellers.

Multi-Vendor sites are similar to shopping malls - A lot of sellers dealing in a similar set of products under one roof. Amazon, Alibaba, Etsy, Walmart, etc., are among the most popular multi-vendor marketplaces.
Some examples of best marketplace platforms are Amazon, Flipkart, Airbnb,  eBay, Paytm Mall, and Meesho.

Multi-Vendors Features  

Vendor Features
Customer can become Vendor.
Customer auto approved/ approval by admin.
Vendor can add and edit Store Information.
Vendor can add and edit Bank and PayPal Details.
Vendor can add visit our Store.
 

Admin Features
Admin can give the configuration settings as show in below list.
Admin can Enabled or Disabled Auto approved Vendor.
Admin can Enabled or Disabled Auto approved Product.
Admin can Enabled or Disabled Auto approved Category.
Admin can Enabled or Disabled Vendor can Add/Edit/Delete review.

If you want to know the price of Multi Vendor Ecommerce and any queries regarding settings, and features, you can contact us at -
Skype: jks0586,
Email: letscmsdev@gmail.com,
Website: www.letscms.com, www.mlmtrees.com,
Call/WhatsApp/WeChat: +91-9717478599.

Free Download Multi Vendor : https://www.opencart.com/index.php?route=marketplace/extension/info&extension_id=43196&filter_search=multivendor
Documentation : https://www.letscms.com/documents/opencart-multivendor-new.html
Visit Website : https://www.letscms.com/blog/multi-vendor
Install : https://www.youtube.com/watch?v=v-86z-gcdb4&list=PLn9cGkS1zw3S9ZBrqbBes7L5fy7VfI8aU

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Get started with your online marketplace by building a multi-vendor e-commerce store

No matter what type of products you sell, we at Appdupe build a more in-depth customer engagement app for your business by delivering a multi-vendor e-commerce script development built on advanced technologies and user-centered design principles. Our dedicated team of designers, developers, analysts, testers, marketers connect with you and gather your requirements to deliver a robust and high-quality multi-vendor e-commerce marketplace store

Read More, https://www.appdupe.com/multi-vendor-ecommerce-script

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