diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..e92cf9b
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+*.pt filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
index 939889d..03e9842 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,8 @@
 # Runtime directories
-ckpt/
 mAP_txt/
 summary/
 weight/
+files/
 
 # IntelliJ IDEA
 .idea/
@@ -12,6 +12,7 @@ weight/
 __pycache__/
 *.py[cod]
 *$py.class
+main
 
 # C extensions
 *.so
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..cfa5aec
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,203 @@
+Copyright 2019 Petr Masopust, Aprar s.r.o..  All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
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+
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e0fcf0d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,87 @@
+# Face recognition technology demo
+
+Mass faces identification and recognition in images. 
+
+## Installation
+
+The simplest complete installation is docker compose: ``docker-compose up -d`` in root directory. For detailed installation instructions look at [API server](apiserver/README.md) or [vectorizer](vectorizer/README.md) readme files.
+Without nvidia docker support docker runs only on cpu with **very** degraded performance (over minute on 6 cpu cores).  
+
+## Usage
+
+### Learn people faces
+
+```shell script
+curl -X POST -F 'person=PID' -F 'directory=DIR' -F 'file=@portrait.jpg' http://localhost:8080/learn
+```
+
+Replace PID with person's id (e.g. database id or name) and DIR with your directory name (e.g. company name). People are recognized only within same directory.  png or jpeg images are supported. Only images with one face are allowed for learning !
+Usually only one good portrait photo is enough but you can learn more photos for each person.
+
+### Recognize people
+
+```shell script
+curl -X POST -F 'directory=DIR' -F 'file=@photo.jpg' http://localhost:8080/recognize
+```
+
+Replace DIR with your directory name (e.g. company name). People are recognized only within same directory. For each detected face the most probable person's id is returned. png or jpeg images are supported. 
+
+Example result:
+
+```json
+{
+"status":"OK",
+"url":"/files/00636b47-e6a5-4fab-8a02-9e44d052c193.jpg",
+"filename":"photo.jpg",
+"directory":"mydir",
+"persons":[
+{"id":"PID1","box":[2797,1164,2918,1285],"score":0.999998927116394,"probability":0.8342},
+{"id":"PID2","box":[2398,1854,2590,2046],"score":0.9999780654907227,"probability":0.32546},
+{"id":"PID3","box":[1753,1148,1905,1300],"score":0.9999217987060547,"probability":0.65785}
+]}
+```
+
+| Field | Description |
+| --- | --- |
+| status | Status message - either OK or error text |
+| url | Relative url to original image |
+| filename | Original image filename |
+| directory | Directory name |
+| persons | Recognized people array |
+| id | Person's id |
+| box | Box around face |
+| score | Face detection score (i.e. probability) |
+| probability | Person recognition probability |
+
+## Architecture
+
+This demo consist of three parts - API server, vectorizer and database. API server is frontend server written in golang.
+Vectorizer is the main part which identifies faces and creates vectors from faces. Database is simple storage for learned vectors.
+Both API server and vectorizer are fully scalable e.g. in kubernetes. The only non scalable part is postgresql database but it can be easily replaced with different storage - e.g. HBase.
+Just reimplement storage.go in API server.
+Only API server listen to customer requests, the rest are internal components and should not be directly accessible from internet. 
+
+## Future roadmap
+
+ * Training on identified faces (both nets are trained separately now)
+ * Face alignment between identification and recognition
+ * Web user interface (help needed !)
+
+## Based on
+
+Github repositories:
+
+* [https://github.com/rainofmine/Face_Attention_Network](https://github.com/rainofmine/Face_Attention_Network)
+* [https://github.com/ronghuaiyang/arcface-pytorch](https://github.com/ronghuaiyang/arcface-pytorch)
+
+Papers:
+
+* [Face Attention Network: An Effective Face Detector for the Occluded Faces](https://arxiv.org/abs/1711.07246)
+* [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://arxiv.org/abs/1905.00292)
+* [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
+* [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063)
+* [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://arxiv.org/abs/1801.09414)
+
+## Licensing
+
+Code in this repository is licensed under the Apache 2.0. See [LICENSE](LICENSE).
diff --git a/apiserver/Dockerfile b/apiserver/Dockerfile
new file mode 100644
index 0000000..9a930a1
--- /dev/null
+++ b/apiserver/Dockerfile
@@ -0,0 +1,20 @@
+FROM golang:alpine AS build-env
+
+RUN apk update && apk upgrade && \
+    apk add --no-cache bash git openssh
+
+COPY ./apiserver /apiserver/apiserver
+COPY ./apiserver.yaml /apiserver/apiserver.yaml
+COPY ./go.mod /apiserver/go.mod
+COPY ./main.go /apiserver/main.go
+
+WORKDIR /apiserver
+RUN go build -o goapp
+
+# final stage
+FROM alpine
+WORKDIR /apiserver
+COPY --from=build-env /apiserver/goapp /apiserver/apiserver
+COPY --from=build-env /apiserver/apiserver.yaml /apiserver/apiserver.yaml
+RUN mkdir /apiserver/files
+ENTRYPOINT /apiserver/apiserver
diff --git a/apiserver/README.md b/apiserver/README.md
new file mode 100644
index 0000000..4d2b745
--- /dev/null
+++ b/apiserver/README.md
@@ -0,0 +1,129 @@
+# API server  
+  
+Frontend server written in golang. **Technology demo - do not use in production !**
+
+**Main purpose:**    
+* serve stored images  
+* send images to vectorizer  
+* store vectors in database  
+* compare vectors and return ids
+
+No local state, can be scaled.
+
+## Configuration
+Edit ``apiserver.yaml`` file:
+
+| Key | Value | Description |
+| --- | --- | --- |
+| port | 8080 | Port to listen |
+| vectorizer | http://vectorizer:8080/vectorize | Vectorizer url |
+| dbuser | faceserver | DB user |
+| dbpassword | secret | DB password |
+| dbname | faceserver | DB name |
+| dbhost | db | DB host |
+
+Do not change configuration if you want run prepared docker-compose.
+
+### DB configuration
+Only postgresql is supported now. Create new role and user:
+```shell script
+createuser -D -P -S faceserver
+createdb -E UTF8 -O faceserver faceserver
+``` 
+
+Create API server tables:
+
+```shell script
+psql -U faceserver -h localhost faceserver <../init.sql
+```
+
+## Instalation
+### Docker image
+Build docker image - preferred method:
+
+```shell script
+docker build -t apiserver:latest .
+```
+
+### Local compilation
+Golang 1.12 is required. Run:
+
+```shell script
+go build main.go
+```
+
+## HTTP API
+### Learn
+
+```shell script
+curl -X POST -F 'person=PID' -F 'directory=DIR' -F 'file=@portrait.jpg' http://localhost:8080/learn
+```
+
+Replace PID with person's id (e.g. database id or name) and DIR with your directory name (e.g. company name). People are recognized only within same directory.  png or jpeg images are supported. Only images with one face are allowed for learning !
+
+Result:
+
+```json
+{
+"status":"OK",
+"url":"/files/01e66d8f-536e-4e5ab3b1-521672739d15.jpg",
+"filename":"photo.jpg",
+"directory":"mydir",
+"persons":[
+{"id":"PID","box":[0,15,65,88],"score":0.9909800887107849}
+]}
+```
+
+|Field|Description|
+|--|--|
+|status|Status message - either OK or error text|
+|url|Relative url to original image|
+|filename|Original image filename|
+|directory|Directory name|
+|persons|Recognized people array|
+|id|Person's id|
+|box|Box around face|
+|score|Face detection score (i.e. probability)|
+
+### Recognize
+
+```shell script
+curl -X POST -F 'directory=DIR' -F 'file=@photo.jpg' http://localhost:8080/recognize
+```
+
+Replace DIR with your directory name (e.g. company name). People are recognized only within same directory. For each detected face the most probable person's id is returned. png or jpeg images are supported. 
+
+Result:
+
+```json
+{
+"status":"OK",
+"url":"/files/00636b47-e6a5-4fab-8a02-9e44d052c193.jpg",
+"filename":"photo.jpg",
+"directory":"mydir",
+"persons":[
+{"id":"PID1","box":[2797,1164,2918,1285],"score":0.999998927116394,"probability":0.8342},
+{"id":"PID2","box":[2398,1854,2590,2046],"score":0.9999780654907227,"probability":0.32546},
+{"id":"PID3","box":[1753,1148,1905,1300],"score":0.9999217987060547,"probability":0.65785}
+]}
+```
+
+| Field | Description |
+| --- | --- |
+| status | Status message - either OK or error text |
+| url | Relative url to original image |
+| filename | Original image filename |
+| directory | Directory name |
+| persons | Recognized people array |
+| id | Person's id |
+| box | Box around face |
+| score | Face detection score (i.e. probability) |
+| probability | Person recognition probability |
+
+### Files
+
+``/files/...`` path contains all learned or recognized images.
+
+## Licensing
+
+Code in this repository is licensed under the Apache 2.0. See [LICENSE](../LICENSE).
diff --git a/apiserver/apiserver.yaml b/apiserver/apiserver.yaml
index 456455d..8bb6587 100644
--- a/apiserver/apiserver.yaml
+++ b/apiserver/apiserver.yaml
@@ -1,8 +1,6 @@
-port: 8081
-vectorizer:
-  url: http://localhost:8080/vectorize
-db:
-  user: faceserver
-  password: aaa
-  name: faceserver
-  host: localhost
+port: 8080
+vectorizer: http://vectorizer:8080/vectorize
+dbuser: faceserver
+dbpassword: secret
+dbname: faceserver
+dbhost: db
diff --git a/apiserver/apiserver/math.go b/apiserver/apiserver/math.go
index b0e0141..b297ae2 100644
--- a/apiserver/apiserver/math.go
+++ b/apiserver/apiserver/math.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package apiserver
 
 import (
diff --git a/apiserver/apiserver/math_test.go b/apiserver/apiserver/math_test.go
index bc3d94f..7780520 100644
--- a/apiserver/apiserver/math_test.go
+++ b/apiserver/apiserver/math_test.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package apiserver
 
 import (
diff --git a/apiserver/apiserver/server.go b/apiserver/apiserver/server.go
index 0d89246..b156111 100644
--- a/apiserver/apiserver/server.go
+++ b/apiserver/apiserver/server.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package apiserver
 
 import (
@@ -166,7 +180,7 @@ func uploadSave(w http.ResponseWriter, r *http.Request) (string, string, []Vecto
 		return "", "", nil, err
 	}
 	defer reader.Close()
-	results, err := Vectorize(uid, reader, viper.GetString("vectorizer.url"))
+	results, err := Vectorize(uid, reader, viper.GetString("vectorizer"))
 	if err != nil {
 		return "", "", nil, err
 	}
diff --git a/apiserver/apiserver/storage.go b/apiserver/apiserver/storage.go
index 242009b..c14fece 100644
--- a/apiserver/apiserver/storage.go
+++ b/apiserver/apiserver/storage.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package apiserver
 
 import (
@@ -25,7 +39,7 @@ type PgStorage struct {
 }
 
 func NewStorage(user string, password string, database string, host string) (PgStorage, error) {
-	connStr := fmt.Sprintf("user=%s dbname=%s password=%s host=%s", user, database, password, host)
+	connStr := fmt.Sprintf("user=%s dbname=%s password=%s host=%s sslmode=disable", user, database, password, host)
 	db, err := sql.Open("postgres", connStr)
 	if err != nil {
 		return PgStorage{}, err
diff --git a/apiserver/apiserver/vectorizer.go b/apiserver/apiserver/vectorizer.go
index 1403094..c6cedd1 100644
--- a/apiserver/apiserver/vectorizer.go
+++ b/apiserver/apiserver/vectorizer.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package apiserver
 
 import (
diff --git a/apiserver/main.go b/apiserver/main.go
index 2c63ad3..5cf8668 100644
--- a/apiserver/main.go
+++ b/apiserver/main.go
@@ -1,3 +1,17 @@
+// Copyright 2019 Petr Masopust, Aprar s.r.o.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 package main
 
 import (
@@ -15,14 +29,12 @@ func main() {
     viper.AddConfigPath("/etc/faceserver/")   // path to look for the config file in
     viper.AddConfigPath("$HOME/.faceserver")  // call multiple times to add many search paths
     viper.AddConfigPath(".")               // optionally look for config in the working directory
-    viper.SetEnvPrefix("AS_")
-    viper.AutomaticEnv()
     err := viper.ReadInConfig() // Find and read the config file
     if err != nil { // Handle errors reading the config file
         panic(fmt.Errorf("Fatal error config file: %s \n", err))
     }
 
-    apiserver.Dbo, err = apiserver.NewStorage(viper.GetString("db.user"), viper.GetString("db.password"), viper.GetString("db.name"), viper.GetString("db.host"))
+    apiserver.Dbo, err = apiserver.NewStorage(viper.GetString("dbuser"), viper.GetString("dbpassword"), viper.GetString("dbname"), viper.GetString("dbhost"))
     if err != nil {
         panic(fmt.Errorf("Fatal error database connection: %s \n", err))
     }
diff --git a/docker-compose.yaml b/docker-compose.yaml
new file mode 100644
index 0000000..1037589
--- /dev/null
+++ b/docker-compose.yaml
@@ -0,0 +1,26 @@
+version: "3.7"
+services:
+  apiserver:
+    build:
+      context: ./apiserver
+    ports:
+      - "8080:8080"
+    depends_on:
+      - db
+      - vectorizer
+  vectorizer:
+    build:
+      context: ./vectorizer
+    environment:
+      VS_PORT: 8080
+      VS_FAN_MODEL: "./ckpt/wider6_10.pt"
+      VS_REC_DEPTH: 50
+      VS_REC_MODEL: "./ckpt/recongition3_37.pt"
+  db:
+    image: postgres:11-alpine
+    environment:
+      POSTGRES_PASSWORD: secret
+      POSTGRES_USER: faceserver
+      POSTGRES_DB: faceserver
+    volumes:
+      - ./init.sql:/docker-entrypoint-initdb.d/init.sql
diff --git a/init.sql b/init.sql
new file mode 100644
index 0000000..a1a5d45
--- /dev/null
+++ b/init.sql
@@ -0,0 +1,11 @@
+CREATE TABLE persons (
+    id character varying(255) NOT NULL,
+    directory character varying(255) NOT NULL,
+    vector double precision[] NOT NULL,
+    filename character varying(255) NOT NULL,
+    filenameuid character varying(255) NOT NULL,
+    box integer[] NOT NULL,
+    score double precision NOT NULL
+);
+
+CREATE INDEX persons_directory ON persons USING btree (directory);
diff --git a/vectorizer/README.md b/vectorizer/README.md
new file mode 100644
index 0000000..dd89b9a
--- /dev/null
+++ b/vectorizer/README.md
@@ -0,0 +1,160 @@
+# Vectorizer 
+  
+Heart of faceserver app. **Technology demo - do not use in production !**
+
+**Main purpose:**    
+* find faces in image
+* create vector from face
+
+No local state, can be scaled. GPU is **highly** recommended.
+
+## Configuration
+
+Set environment variables:
+
+| Key | Default value | Description |
+| --- | --- | --- |
+| VS_PORT | 8080 | Port to listen (for Flask) |
+| VS_FAN_MODEL |  | Path to identification model |
+| VS_REC_DEPTH | 50 | Recognition net depth |
+| VS_REC_MODEL |  | Path to recognition model |
+
+Do not change configuration if you want run prepared docker-compose.
+
+## Instalation
+
+### Docker image
+
+Build docker image - preferred method if you have nvidia-docker:
+```shell script
+docker build -t vectorizer:latest .
+```
+
+### Local installation
+
+Install PIP dependencies (virtualenv recommended):
+
+```shell script
+pip install --upgrade -r requirements.txt
+```
+
+And then run server:
+
+```shell script
+python3 -m vectorizer.server
+```
+
+## HTTP API
+
+### Vectorization
+
+```shell script
+curl -X POST -F 'file=@image.jpg' http://localhost:8080/vectorize
+```
+
+png or jpeg images are supported.
+
+Result:
+
+```json
+[
+{"box":[0,15,65,88],"vector":[-0.14234,...,0.32432],"score":0.9909800887107849}
+]
+```
+
+| Field | Description |
+| --- | --- |
+| box | Box around face |
+| vector | Array of 512 floats |
+| score | Face detection score (i.e. probability) |
+
+## Training
+
+**GPU is mandatory for training !** 
+Training takes at least several days to achieve reasonable accuracy on single RTX 2070.
+Trained models are stored in ``ckpt`` directory. Pretrained models with example parameters are included.
+
+### Identification
+
+Example:
+
+```shell script
+python3 -m identification.train --wider_train ~/datasets/wider/wider_face_train_bbx_gt.txt \
+--wider_train_prefix ~/datasets/wider/WIDER_train/images \
+--wider_val ~/datasets/wider/wider_face_val_bbx_gt.txt \
+--wider_val_prefix ~/datasets/wider/WIDER_val/images \
+--depth 50 --epochs 30 --batch_size 1 --model_name wider1
+```
+
+| Argument | Description | Required / Default value |
+| --- | --- | --- |
+| --wider_train | Path to file containing WIDER training annotations (wider_face_train_bbx_gt.txt) | Yes |
+| --wider_val | Path to file containing WIDER validation annotations (wider_face_val_bbx_gt.txt) |  |
+| --wider_train_prefix | Prefix path to WIDER train images | Yes |
+| --wider_val_prefix | Prefix path to WIDER validation images |  |
+| --depth | Resnet depth, must be one of 18, 34, 50, 101, 152 | 50 |
+| --epochs | Number of epochs | 50 |
+| --batch_size | Batch size - increase only if you have enough GPU memory (i.e. >16 GB) ! | 2 |
+| --model_name | Model name prefix | Yes |
+| --parallel | Run training with DataParallel | false |
+| --pretrained | Pretrained model (e.g. for crash recovery) |  |
+
+There is also option to train from csv files - see train.py and dataloader.py for details.
+
+### Recognition
+
+Example:
+
+```shell script
+python3 -m recognition.train \
+--casia_list ~/datasets/CASIA-maxpy-clean/train.txt \
+--casia_root ~/datasets/CASIA-maxpy-clean \
+--lfw_root ~/datasets/lfw \
+--lfw_pair_list lfw_test_pair.txt \
+--model_name recongition1 --batch_size 20 \
+--loss adacos --print_freq 20 --depth 50
+```
+
+| Argument | Description | Required / Default value |
+| --- | --- | --- |
+| --casia_list | Path to CASIA dataset file list (train.txt) | Yes |
+| --casia_root | Path to CASIA images | Yes |
+| --lfw_root | Path to LFW dataset | Yes |
+| --lfw_pair_list | Path to LFW pair list file (lfw_test_pair.txt - in this repository) | Yes |
+| --depth | Resnet depth, must be one of 18, 34, 50, 101, 152 or 20 for sphere net | 50 |
+| --epochs | Number of epochs | 50 |
+| --batch_size | Batch size | 16 |
+| --model_name | Model name prefix | Yes |
+| --parallel | Run training with DataParallel | false |
+| --loss | One of focal_loss. cross_entropy, arcface, cosface, sphereface, adacos | cross_entropy |
+| --optimizer | One of sgd, adam | sgd |
+| --weight_decay | Weight decay | 0.0005 |
+| --lr | Learning rate | 0.1 |
+| --lr_step | Learning rate step | 10 |
+| --easy_margin | Use easy margin | false |
+| --print_freq | Print every N batch | 100 |
+
+## Datasets for training
+
+* [WIDER](http://shuoyang1213.me/WIDERFACE/)
+* [LFW](http://vis-www.cs.umass.edu/lfw/)
+* CASIA maxpy clean - no official web but can be downloaded from suspicious sites (use google)
+
+## Based on
+
+Github repositories:
+
+* [https://github.com/rainofmine/Face_Attention_Network](https://github.com/rainofmine/Face_Attention_Network)
+* [https://github.com/ronghuaiyang/arcface-pytorch](https://github.com/ronghuaiyang/arcface-pytorch)
+
+Papers:
+
+* [Face Attention Network: An Effective Face Detector for the Occluded Faces](https://arxiv.org/abs/1711.07246)
+* [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://arxiv.org/abs/1905.00292)
+* [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
+* [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063)
+* [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://arxiv.org/abs/1801.09414)
+
+## Licensing
+
+Code in this repository is licensed under the Apache 2.0. See [LICENSE](../LICENSE).
diff --git a/vectorizer/ckpt/recongition3_37.pt b/vectorizer/ckpt/recongition3_37.pt
new file mode 100644
index 0000000..f959a14
--- /dev/null
+++ b/vectorizer/ckpt/recongition3_37.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60c4b6850c06c00b086e0e3918e089f6bb181f0330a9ce1b60ac184e5b09c6e0
+size 98498540
diff --git a/vectorizer/ckpt/wider6_10.pt b/vectorizer/ckpt/wider6_10.pt
new file mode 100644
index 0000000..b649b55
--- /dev/null
+++ b/vectorizer/ckpt/wider6_10.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0c8b9095c6b85905e7236b253db4c445113ee5fccc272e558d65d52ab4c7523
+size 155109396
diff --git a/vectorizer/identification/anchors.py b/vectorizer/identification/anchors.py
index 791cad0..65de358 100644
--- a/vectorizer/identification/anchors.py
+++ b/vectorizer/identification/anchors.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import numpy as np
 import torch
 import torch.nn as nn
diff --git a/vectorizer/identification/csv_eval.py b/vectorizer/identification/csv_eval.py
index ac1f68e..e388431 100644
--- a/vectorizer/identification/csv_eval.py
+++ b/vectorizer/identification/csv_eval.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import numpy as np
 
 import torch
diff --git a/vectorizer/identification/dataloader.py b/vectorizer/identification/dataloader.py
index 273466d..f981a88 100644
--- a/vectorizer/identification/dataloader.py
+++ b/vectorizer/identification/dataloader.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import torch
 import numpy as np
 import random
@@ -366,8 +385,9 @@ class Resizer(object):
 
         # resize the image with the computed scale
 
-        image = np.array(image.resize((int(round((cols * scale))), int(round((rows * scale)))), resample=Image.BILINEAR))
-        image = image  / 255.0
+        image = np.array(
+            image.resize((int(round((cols * scale))), int(round((rows * scale)))), resample=Image.BILINEAR))
+        image = image / 255.0
 
         rows, cols, cns = image.shape
 
diff --git a/vectorizer/identification/detector.py b/vectorizer/identification/detector.py
index 76bc7e0..1568905 100644
--- a/vectorizer/identification/detector.py
+++ b/vectorizer/identification/detector.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import numpy as np
 import torch
 import argparse
@@ -20,7 +39,7 @@ def fan_detect(model, img_data, threshold=0.9, max_detections=100, is_cuda=True)
             img_data = img_data.cuda()
         scores, labels, boxes = model(img_data)
         if scores is None:
-            return np.empty((0,0)), np.empty((0,0))
+            return np.empty((0, 0)), np.empty((0, 0))
 
         scores = scores.cpu().numpy()
         scale = transformed['scale']
@@ -49,7 +68,7 @@ def load_model(model_path, is_cuda=True):
     if is_cuda:
         model = model.cuda()
 
-    model.anchors.is_cuda=is_cuda
+    model.anchors.is_cuda = is_cuda
 
     return model
 
diff --git a/vectorizer/identification/losses.py b/vectorizer/identification/losses.py
index 9e63f34..d4c1235 100644
--- a/vectorizer/identification/losses.py
+++ b/vectorizer/identification/losses.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import math
 import torch
 import torch.nn as nn
@@ -8,6 +27,7 @@ def memprint(a):
     print(a.shape)
     print(a.element_size() * a.nelement())
 
+
 def calc_iou(a, b):
     step = 20
     IoU = torch.zeros((len(a), len(b))).cuda()
@@ -18,11 +38,11 @@ def calc_iou(a, b):
     area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])
 
     for i in range(step_count):
-        iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[i * step:(i+1) * step, 2])
-        iw.sub_(torch.max(torch.unsqueeze(a[:, 0], 1), b[i * step:(i+1) * step, 0]))
+        iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[i * step:(i + 1) * step, 2])
+        iw.sub_(torch.max(torch.unsqueeze(a[:, 0], 1), b[i * step:(i + 1) * step, 0]))
 
-        ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[i * step:(i+1) * step, 3])
-        ih.sub_(torch.max(torch.unsqueeze(a[:, 1], 1), b[i * step:(i+1) * step, 1]))
+        ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[i * step:(i + 1) * step, 3])
+        ih.sub_(torch.max(torch.unsqueeze(a[:, 1], 1), b[i * step:(i + 1) * step, 1]))
 
         iw.clamp_(min=0)
         ih.clamp_(min=0)
@@ -30,12 +50,12 @@ def calc_iou(a, b):
         iw.mul_(ih)
         del ih
 
-        ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area[i * step:(i+1) * step] - iw
+        ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area[i * step:(i + 1) * step] - iw
         ua = torch.clamp(ua, min=1e-8)
         iw.div_(ua)
         del ua
 
-        IoU[:, i * step:(i+1) * step] = iw
+        IoU[:, i * step:(i + 1) * step] = iw
 
     return IoU
 
@@ -131,7 +151,7 @@ class FocalLoss(nn.Module):
                 alpha_factor = torch.ones(targets.shape)
                 if self.is_cuda:
                     alpha_factor = alpha_factor.cuda()
-                alpha_factor *=  alpha
+                alpha_factor *= alpha
             except:
                 print(targets)
                 print(targets.shape)
diff --git a/vectorizer/identification/model_level_attention.py b/vectorizer/identification/model_level_attention.py
index 1bcd663..b85f184 100644
--- a/vectorizer/identification/model_level_attention.py
+++ b/vectorizer/identification/model_level_attention.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import torch.nn as nn
 import torch
 import math
diff --git a/vectorizer/identification/train.py b/vectorizer/identification/train.py
index 6234933..b161668 100644
--- a/vectorizer/identification/train.py
+++ b/vectorizer/identification/train.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import argparse
 import collections
 import os
@@ -12,7 +31,8 @@ import torch.utils.model_zoo as model_zoo
 from identification.model_level_attention import resnet18, resnet34, resnet50, resnet101, resnet152
 from torch.utils.data import DataLoader
 from identification.csv_eval import evaluate
-from identification.dataloader import WIDERDataset, AspectRatioBasedSampler, collater, Resizer, Augmenter, Normalizer, CSVDataset
+from identification.dataloader import WIDERDataset, AspectRatioBasedSampler, collater, Resizer, Augmenter, Normalizer, \
+    CSVDataset
 
 is_cuda = torch.cuda.is_available()
 print('CUDA available: {}'.format(is_cuda))
@@ -27,6 +47,7 @@ model_urls = {
 
 ckpt = False
 
+
 def main(args=None):
     parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
 
@@ -154,9 +175,11 @@ def main(args=None):
                 img_data = img_data.cuda()
                 annot_data = annot_data.cuda()
 
-            print("GPU memory allocated: %d max memory allocated: %d memory cached: %d max memory cached: %d" % (torch.cuda.memory_allocated() / 1024**2, torch.cuda.max_memory_allocated() / 1024**2, torch.cuda.memory_cached() / 1024**2, torch.cuda.max_memory_cached() / 1024**2))
+            print("GPU memory allocated: %d max memory allocated: %d memory cached: %d max memory cached: %d" % (
+            torch.cuda.memory_allocated() / 1024 ** 2, torch.cuda.max_memory_allocated() / 1024 ** 2,
+            torch.cuda.memory_cached() / 1024 ** 2, torch.cuda.max_memory_cached() / 1024 ** 2))
             classification_loss, regression_loss, mask_loss = retinanet([img_data, annot_data])
-            
+
             del img_data
             del annot_data
 
@@ -195,7 +218,7 @@ def main(args=None):
 
         scheduler.step(np.mean(epoch_loss))
 
-        #TODO remove makedir
+        # TODO remove makedir
         os.makedirs('./ckpt', exist_ok=True)
         if parser.parallel:
             torch.save(retinanet.module, './ckpt/' + parser.model_name + '_{}.pt'.format(epoch_num))
diff --git a/vectorizer/identification/utils.py b/vectorizer/identification/utils.py
index dd6b076..c8bf2f7 100644
--- a/vectorizer/identification/utils.py
+++ b/vectorizer/identification/utils.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/rainofmine/Face_Attention_Network
+"""
+
 import torch
 import torch.nn as nn
 import numpy as np
diff --git a/vectorizer/recognition/angle.py b/vectorizer/recognition/angle.py
index cbb274d..0b3c661 100644
--- a/vectorizer/recognition/angle.py
+++ b/vectorizer/recognition/angle.py
@@ -1,4 +1,21 @@
 # -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
+"""
 
 import math
 
@@ -32,18 +49,18 @@ class AdaCos(nn.Module):
             self.criterion = self.criterion.cuda()
 
     def forward(self, input, label):
-# changed to fixed adacos
-#        theta = torch.acos(torch.clamp(input, -1.0 + 1e-7, 1.0 - 1e-7))
-#        one_hot = torch.zeros_like(input)
-#        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
-#        with torch.no_grad():
-#            B_avg = torch.where(one_hot < 1, torch.exp(self.s * input), torch.zeros_like(input))
-#            B_avg = torch.sum(B_avg) / input.size(0)
-#            theta_med = torch.median(theta)
-#            self.s = torch.log(B_avg) / torch.cos(torch.min(math.pi/4 * torch.ones_like(theta_med), theta_med))
-#            # TODO why converge to infinity ?
-#            self.s = torch.clamp(self.s, self.base_s / 2, self.base_s * 2)
-#            print(self.s)
+        # changed to fixed adacos - faster and more stable
+        #        theta = torch.acos(torch.clamp(input, -1.0 + 1e-7, 1.0 - 1e-7))
+        #        one_hot = torch.zeros_like(input)
+        #        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        #        with torch.no_grad():
+        #            B_avg = torch.where(one_hot < 1, torch.exp(self.s * input), torch.zeros_like(input))
+        #            B_avg = torch.sum(B_avg) / input.size(0)
+        #            theta_med = torch.median(theta)
+        #            self.s = torch.log(B_avg) / torch.cos(torch.min(math.pi/4 * torch.ones_like(theta_med), theta_med))
+        #            # TODO why converge to infinity ?
+        #            self.s = torch.clamp(self.s, self.base_s / 2, self.base_s * 2)
+        #            print(self.s)
         output = self.s * input
 
         return self.criterion(output, label)
diff --git a/vectorizer/recognition/focal_loss.py b/vectorizer/recognition/focal_loss.py
index 39e42d7..e8152d3 100644
--- a/vectorizer/recognition/focal_loss.py
+++ b/vectorizer/recognition/focal_loss.py
@@ -1,8 +1,24 @@
 # -*- coding: utf-8 -*-
 """
-Created on 18-6-7 上午10:11
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
 
-@author: ronghuaiyang
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
+
+   Created on 18-6-7 上午10:11
+
+   @author: ronghuaiyang
 """
 
 import torch
diff --git a/vectorizer/recognition/nets.py b/vectorizer/recognition/nets.py
index 96a7744..d48fdf0 100644
--- a/vectorizer/recognition/nets.py
+++ b/vectorizer/recognition/nets.py
@@ -1,3 +1,26 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
+
+   Created on 18-5-21 下午5:26
+
+   @author: ronghuaiyang
+"""
+
 import torchvision.models as models
 from torch import nn
 
@@ -46,67 +69,85 @@ def resnet152(pretrained=False, **kwargs):
     model = models.resnet152(num_classes=512, **kwargs)
     return model
 
+
 def sphere20():
     return sphere20a()
 
 
+def get_net_by_depth(depth):
+    if depth == 18:
+        model = resnet18()
+    elif depth == 20:
+        model = sphere20()
+    elif depth == 34:
+        model = resnet34()
+    elif depth == 50:
+        model = resnet50()
+    elif depth == 101:
+        model = resnet101()
+    elif depth == 152:
+        model = resnet152()
+    else:
+        raise ValueError('Unsupported model depth %d, must be one of 18, 34, 50, 101, 152' % depth)
+
+    return model
+
+
 class sphere20a(nn.Module):
     def __init__(self):
         super(sphere20a, self).__init__()
-        #input = B*3*112*96
-        self.conv1_1 = nn.Conv2d(3,64,3,2,1) #=>B*64*56*48
+        # input = B*3*112*96
+        self.conv1_1 = nn.Conv2d(3, 64, 3, 2, 1)  # =>B*64*56*48
         self.relu1_1 = nn.PReLU(64)
-        self.conv1_2 = nn.Conv2d(64,64,3,1,1)
+        self.conv1_2 = nn.Conv2d(64, 64, 3, 1, 1)
         self.relu1_2 = nn.PReLU(64)
-        self.conv1_3 = nn.Conv2d(64,64,3,1,1)
+        self.conv1_3 = nn.Conv2d(64, 64, 3, 1, 1)
         self.relu1_3 = nn.PReLU(64)
 
-        self.conv2_1 = nn.Conv2d(64,128,3,2,1) #=>B*128*28*24
+        self.conv2_1 = nn.Conv2d(64, 128, 3, 2, 1)  # =>B*128*28*24
         self.relu2_1 = nn.PReLU(128)
-        self.conv2_2 = nn.Conv2d(128,128,3,1,1)
+        self.conv2_2 = nn.Conv2d(128, 128, 3, 1, 1)
         self.relu2_2 = nn.PReLU(128)
-        self.conv2_3 = nn.Conv2d(128,128,3,1,1)
+        self.conv2_3 = nn.Conv2d(128, 128, 3, 1, 1)
         self.relu2_3 = nn.PReLU(128)
 
-        self.conv2_4 = nn.Conv2d(128,128,3,1,1) #=>B*128*28*24
+        self.conv2_4 = nn.Conv2d(128, 128, 3, 1, 1)  # =>B*128*28*24
         self.relu2_4 = nn.PReLU(128)
-        self.conv2_5 = nn.Conv2d(128,128,3,1,1)
+        self.conv2_5 = nn.Conv2d(128, 128, 3, 1, 1)
         self.relu2_5 = nn.PReLU(128)
 
-
-        self.conv3_1 = nn.Conv2d(128,256,3,2,1) #=>B*256*14*12
+        self.conv3_1 = nn.Conv2d(128, 256, 3, 2, 1)  # =>B*256*14*12
         self.relu3_1 = nn.PReLU(256)
-        self.conv3_2 = nn.Conv2d(256,256,3,1,1)
+        self.conv3_2 = nn.Conv2d(256, 256, 3, 1, 1)
         self.relu3_2 = nn.PReLU(256)
-        self.conv3_3 = nn.Conv2d(256,256,3,1,1)
+        self.conv3_3 = nn.Conv2d(256, 256, 3, 1, 1)
         self.relu3_3 = nn.PReLU(256)
 
-        self.conv3_4 = nn.Conv2d(256,256,3,1,1) #=>B*256*14*12
+        self.conv3_4 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
         self.relu3_4 = nn.PReLU(256)
-        self.conv3_5 = nn.Conv2d(256,256,3,1,1)
+        self.conv3_5 = nn.Conv2d(256, 256, 3, 1, 1)
         self.relu3_5 = nn.PReLU(256)
 
-        self.conv3_6 = nn.Conv2d(256,256,3,1,1) #=>B*256*14*12
+        self.conv3_6 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
         self.relu3_6 = nn.PReLU(256)
-        self.conv3_7 = nn.Conv2d(256,256,3,1,1)
+        self.conv3_7 = nn.Conv2d(256, 256, 3, 1, 1)
         self.relu3_7 = nn.PReLU(256)
 
-        self.conv3_8 = nn.Conv2d(256,256,3,1,1) #=>B*256*14*12
+        self.conv3_8 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
         self.relu3_8 = nn.PReLU(256)
-        self.conv3_9 = nn.Conv2d(256,256,3,1,1)
+        self.conv3_9 = nn.Conv2d(256, 256, 3, 1, 1)
         self.relu3_9 = nn.PReLU(256)
 
-        self.conv4_1 = nn.Conv2d(256,512,3,2,1) #=>B*512*7*6
+        self.conv4_1 = nn.Conv2d(256, 512, 3, 2, 1)  # =>B*512*7*6
         self.relu4_1 = nn.PReLU(512)
-        self.conv4_2 = nn.Conv2d(512,512,3,1,1)
+        self.conv4_2 = nn.Conv2d(512, 512, 3, 1, 1)
         self.relu4_2 = nn.PReLU(512)
-        self.conv4_3 = nn.Conv2d(512,512,3,1,1)
+        self.conv4_3 = nn.Conv2d(512, 512, 3, 1, 1)
         self.relu4_3 = nn.PReLU(512)
 
-        self.fc5 = nn.Linear(512*14*14,512)
+        self.fc5 = nn.Linear(512 * 14 * 14, 512)
         # ORIGINAL for 112x96: self.fc5 = nn.Linear(512*7*6,512)
 
-
     def forward(self, x):
         x = self.relu1_1(self.conv1_1(x))
         x = x + self.relu1_3(self.conv1_3(self.relu1_2(self.conv1_2(x))))
@@ -124,6 +165,6 @@ class sphere20a(nn.Module):
         x = self.relu4_1(self.conv4_1(x))
         x = x + self.relu4_3(self.conv4_3(self.relu4_2(self.conv4_2(x))))
 
-        x = x.view(x.size(0),-1)
+        x = x.view(x.size(0), -1)
         x = self.fc5(x)
         return x
diff --git a/vectorizer/recognition/test.py b/vectorizer/recognition/test.py
index b2f279c..b80b36b 100644
--- a/vectorizer/recognition/test.py
+++ b/vectorizer/recognition/test.py
@@ -1,22 +1,37 @@
 # -*- coding: utf-8 -*-
 """
-Created on 18-5-30 下午4:55
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
 
-@author: ronghuaiyang
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
+
+   Created on 18-5-30 下午4:55
+
+   @author: ronghuaiyang
 """
 import os
 import argparse
 
 from torch.utils.data import TensorDataset, DataLoader
 
-from recognition.nets import resnet18, resnet34, resnet50, resnet101, resnet152, sphere20
+from recognition.nets import get_net_by_depth
 import torch
 import numpy as np
 from torch.nn import DataParallel
 from PIL import Image
 from torchvision import transforms as T
 
-
 imagesize = 224
 batch_size = 20
 
@@ -120,7 +135,8 @@ def cal_accuracy(y_score, y_true):
 def main(args=None):
     parser = argparse.ArgumentParser(description='Testing script for face identification.')
 
-    parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152 or 20 for sphere', type=int, default=50)
+    parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152 or 20 for sphere', type=int,
+                        default=50)
     parser.add_argument('--parallel', help='Run training with DataParallel', dest='parallel',
                         default=False, action='store_true')
     parser.add_argument('--model', help='Path to model')
@@ -133,20 +149,7 @@ def main(args=None):
     is_cuda = torch.cuda.is_available()
     print('CUDA available: {}'.format(is_cuda))
 
-    if parser.depth == 18:
-        model = resnet18()
-    elif parser.depth == 20:
-        model = sphere20()
-    elif parser.depth == 34:
-        model = resnet34()
-    elif parser.depth == 50:
-        model = resnet50()
-    elif parser.depth == 101:
-        model = resnet101()
-    elif parser.depth == 152:
-        model = resnet152()
-    else:
-        raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
+    model = get_net_by_depth(parser.depth)
 
     if parser.parallel:
         model = DataParallel(model)
diff --git a/vectorizer/recognition/train.py b/vectorizer/recognition/train.py
index 141e557..45dbf69 100644
--- a/vectorizer/recognition/train.py
+++ b/vectorizer/recognition/train.py
@@ -1,3 +1,22 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
+"""
+
 import argparse
 import os
 import time
@@ -11,7 +30,7 @@ from torchvision import transforms as T
 
 from recognition.angle import AngleLinear, CosFace, SphereFace, ArcFace, AdaCos
 from recognition.focal_loss import FocalLoss
-from recognition.nets import resnet18, resnet34, resnet50, resnet101, resnet152, sphere20
+from recognition.nets import get_net_by_depth
 from recognition.test import lfw_test2, get_pair_list, load_img_data
 
 
@@ -25,7 +44,7 @@ class Dataset(torch.utils.data.Dataset):
         self.imgs = np.random.permutation(imgs)
 
         normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
-                                 std=[0.229, 0.224, 0.225])
+                                std=[0.229, 0.224, 0.225])
 
         self.transforms = T.Compose([
             T.RandomResizedCrop(imagesize),
@@ -62,14 +81,17 @@ def main(args=None):
 
     parser.add_argument('--print_freq', help='Print every N batch (default 100)', type=int, default=100)
     parser.add_argument('--epochs', help='Number of epochs', type=int, default=50)
-    parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152 or 20 for sphere', type=int, default=50)
+    parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152 or 20 for sphere', type=int,
+                        default=50)
     parser.add_argument('--lr_step', help='Learning rate step (default 10)', type=int, default=10)
     parser.add_argument('--lr', help='Learning rate (default 0.1)', type=float, default=0.1)
     parser.add_argument('--weight_decay', help='Weight decay (default 0.0005)', type=float, default=0.0005)
-    parser.add_argument('--easy_margin', help='Use easy margin (default false)', dest='easy_margin', default=False, action='store_true')
+    parser.add_argument('--easy_margin', help='Use easy margin (default false)', dest='easy_margin', default=False,
+                        action='store_true')
     parser.add_argument('--parallel', help='Run training with DataParallel', dest='parallel',
                         default=False, action='store_true')
-    parser.add_argument('--loss', help='One of focal_loss. cross_entropy, arcface, cosface, sphereface, adacos (default cross_entropy)',
+    parser.add_argument('--loss',
+                        help='One of focal_loss. cross_entropy, arcface, cosface, sphereface, adacos (default cross_entropy)',
                         type=str, default='cross_entropy')
     parser.add_argument('--optimizer', help='One of sgd, adam (default sgd)',
                         type=str, default='sgd')
@@ -86,20 +108,7 @@ def main(args=None):
     print('CUDA available: {}'.format(is_cuda))
 
     imagesize = 224
-    if parser.depth == 18:
-        model = resnet18()
-    elif parser.depth == 20:
-        model = sphere20()
-    elif parser.depth == 34:
-        model = resnet34()
-    elif parser.depth == 50:
-        model = resnet50()
-    elif parser.depth == 101:
-        model = resnet101()
-    elif parser.depth == 152:
-        model = resnet152()
-    else:
-        raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
+    model = get_net_by_depth(parser.depth)
 
     # TODO split training dataset to train/validation and stop using test dataset for acc
     train_dataset = Dataset(parser.casia_root, parser.casia_list, imagesize)
@@ -191,7 +200,7 @@ def main(args=None):
         acc = lfw_test2(model, identity_list, img_data, is_cuda=is_cuda)
         print('Accuracy: %f' % acc)
         if last_acc < acc:
-            #TODO remove makedir
+            # TODO remove makedir
             os.makedirs('./ckpt', exist_ok=True)
             torch.save(model.state_dict(), './ckpt/' + parser.model_name + '_{}.pt'.format(i))
             torch.save(metric_fc.state_dict(), './ckpt/' + parser.model_name + '_metric_{}.pt'.format(i))
diff --git a/vectorizer/train-ident.sh b/vectorizer/train-ident.sh
new file mode 100755
index 0000000..1749d09
--- /dev/null
+++ b/vectorizer/train-ident.sh
@@ -0,0 +1,3 @@
+python3 -m identification.train --wider_train ~/datasets/wider/wider_face_train_bbx_gt.txt --wider_train_prefix ~/datasets/wider/WIDER_train/images \
+--wider_val ~/datasets/wider/wider_face_val_bbx_gt.txt --wider_val_prefix ~/datasets/wider/WIDER_val/images \
+--depth 50 --epochs 30 --batch_size 1 --model_name wider1
diff --git a/vectorizer/train-rec.sh b/vectorizer/train-rec.sh
index 4c502fd..53abbd9 100755
--- a/vectorizer/train-rec.sh
+++ b/vectorizer/train-rec.sh
@@ -1,2 +1,2 @@
-python3 -m recognition.train --casia_list /home/ehp/tmp/datasets/CASIA-maxpy-clean/train.txt --casia_root /home/ehp/tmp/datasets/CASIA-maxpy-clean --lfw_root /home/ehp/tmp/datasets/lfw \
---lfw_pair_list /home/ehp/git/arcface/lfw_test_pair.txt --model_name recongition3 --batch_size 20 --loss adacos --print_freq 20 --depth 50
+python3 -m recognition.train --casia_list ~/datasets/CASIA-maxpy-clean/train.txt --casia_root ~/datasets/CASIA-maxpy-clean --lfw_root ~/datasets/lfw \
+--lfw_pair_list lfw_test_pair.txt --model_name recongition1 --batch_size 20 --loss adacos --print_freq 20 --depth 50
diff --git a/vectorizer/train.sh b/vectorizer/train.sh
deleted file mode 100755
index 8bebf37..0000000
--- a/vectorizer/train.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#python3 -m identification.train --wider_train /home/ehp/tmp/datasets/wider/sample.txt --wider_train_prefix /home/ehp/tmp/datasets/wider/sample/images \
-#--wider_val /home/ehp/tmp/datasets/wider/sample_val.txt --wider_val_prefix /home/ehp/tmp/datasets/wider/sample_val/images \
-#--depth 50 --epochs 30 --batch_size 1 --model_name wider_sample1
-
-python3 -m identification.train --wider_train /home/ehp/tmp/datasets/wider/wider_face_train_bbx_gt.txt --wider_train_prefix /home/ehp/tmp/datasets/wider/WIDER_train/images \
---wider_val /home/ehp/tmp/datasets/wider/wider_face_val_bbx_gt.txt --wider_val_prefix /home/ehp/tmp/datasets/wider/WIDER_val/images \
---depth 50 --epochs 30 --batch_size 1 --model_name widernew1
diff --git a/vectorizer/vectorizer/server.py b/vectorizer/vectorizer/server.py
index 4b24353..1589596 100644
--- a/vectorizer/vectorizer/server.py
+++ b/vectorizer/vectorizer/server.py
@@ -1,3 +1,20 @@
+# -*- coding: utf-8 -*-
+"""
+   Copyright 2019 Petr Masopust, Aprar s.r.o.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+"""
+
 import logging
 import os
 import sys
@@ -7,18 +24,25 @@ from flask import Flask, request, abort, jsonify
 from werkzeug.utils import secure_filename
 
 import torch
-from recognition.nets import resnet50
+from recognition.nets import get_net_by_depth
 from torchvision import transforms as T
 from PIL import Image
 import identification.detector as fan
 
 is_cuda = torch.cuda.is_available()
 print('CUDA: %s' % is_cuda)
-fan_model = fan.load_model('ckpt/wider6_10.pt', is_cuda=is_cuda)
+
+fan_file = os.environ.get('VS_FAN_MODEL', None)
+if fan_file is None:
+    raise Exception('VS_FAN_MODEL is mandatory parameter')
+fan_model = fan.load_model(fan_file, is_cuda=is_cuda)
 
 # load recognition model
-rec_model = resnet50()
-rec_model.load_state_dict(torch.load('ckpt/recongition3_37.pt', map_location=lambda storage, location: storage))
+rec_model = get_net_by_depth(int(os.environ.get('VS_REC_DEPTH', 50)))
+rec_file = os.environ.get('VS_REC_MODEL', None)
+if rec_file is None:
+    raise Exception('VS_REC_MODEL is mandatory parameter')
+rec_model.load_state_dict(torch.load(rec_file, map_location=lambda storage, location: storage))
 rec_model.eval()
 if is_cuda:
     rec_model = rec_model.cuda()
@@ -38,6 +62,7 @@ app = Flask(__name__)
 UPLOAD_FOLDER = tempfile.gettempdir()
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 
+
 def compute_vector(data):
     with torch.no_grad():
         data = transforms(data)
@@ -75,7 +100,8 @@ def upload_file():
                     boxes = boxes.astype(int)
                     scores = scores.astype(float)
                     extracted = [{'box': arr.tolist(),
-                                  'vector': compute_vector(img.crop((arr[0], arr[1], arr[2], arr[3]))).squeeze().tolist(),
+                                  'vector': compute_vector(
+                                      img.crop((arr[0], arr[1], arr[2], arr[3]))).squeeze().tolist(),
                                   'score': score
                                   } for arr, score in zip(boxes, scores)]
                     return jsonify(extracted)
@@ -87,4 +113,5 @@ def upload_file():
 
 if __name__ == '__main__':
     logging.basicConfig()
-    app.run(host='0.0.0.0', debug=False, port=8080)
+    port = int(os.environ.get('VS_PORT', '8080'))
+    app.run(host='0.0.0.0', debug=False, port=port)