Amazon Go /Big Basket Smart Machine - Tech Analysis

Amazon Go

• QR Code for user account linking
• Pick items, Auto detected
• Multiple validation points (Video Object Detection, Shelf Weight sensor-based confirmation, RFID reads etc)
• Multiple RFID readers to capture item movement across ISLEs

Cons

• Massive surveillance 
• Real-time computation

IMO, Big Basket Smart Machine is also similar to the implementation

• QR Code for user account linking
• Pick and Buy
• Mobile App integration 
• Weight Sensors used to detect Shelf Item Quantity
• Unique Items in each row. It is not mixed

Since its a standalone machine, it will not need any tracking with RFID / Video camera. Only Weight sensors sufficient and it is a single person operated at a time.

Happy Learning!!!

Day #299 - OpenVino Python Code for Faces - Pedestrain - Attributes

Hope this helps for other developers trying out executing intel openvino models in python

Happy Learning!!!

Day #298 - Data Analysis of PNB Defaulters

Data Source - Link

Data Analysis of PNB Defaulters

Chart #1 - Top 20 States By Company Registration State and Defaulted Amount



Chart #2 - Top 20 States By Defaulters Count





Chart #3 - Top 20 Branches with Maximum Defaulters





Chart #4 - Top 20 Branches with Maximum Defaulters Loan Value





Possible Feature Variables

1. Branch Related Approval Score, Higher Defaulters lower the rating
2. Similar Industry Match Score
3. State Related Scores
4. Connections / Joint ventures in Past with Collapsed Companies
5. Rules for threshold limit based on Industry / State / Branch
6. Multiple Models for ongoing monitoring / performance / social medial trends etc..
7. Build a global model with defaulter list across banks and identify common patterns/modus operandi

Happy Learning!!!

Day #297 - Paper Analysis - WIDER Face and Pedestrian Challenge

WIDER Face and Pedestrian Challenge
Tasks - face detection, pedestrian detection, person search
Dataset - WIDER Pedestrian Track - 20,000 images. From surveillance cameras, driving vehicles

Face Detection

• Approach 1 -  single stage detector with the network structure based on RetinaNet [7] and FAN - Face attention network
• Approach 2 - two-stage face detector following Faster R-CNN [12] and FPN Feature pyramid networks [13] framework
• Approach 3 - a two-stage face detection framework. RetinaNet [7] and RefineDet [15]. The team uses two-stage classification and regression to improve the accuracy of classification

PEDESTRIAN DETECTION TRACK

• Approach 1 - basic detection framework of the champion is Cascade R-CNN. Five models are ensembled: ResNet50 [18], DenseNet-161 [19], 197 SENet-154 [20] and two ResNext-101 [21] models.
• Approach 2 - The second team uses FPN [13] and Faster R-CNN [12] as the basis of their detection framework
• Approach 3 - The team at the third place uses Cascade R-CNN [16] as the detection framework

PERSON SEARCH TRACK

• Approach 1 - The winning team designs a cascaded model that utilizes both face and body features for person search. (1) The face detector used here is MTCNN [26] trained on WIDER FACE [4]. (2) The face recognition model backbones include ResNet [18], InceptionResNet-v2 [27], DenseNet [19], DPN and MobiletNet [28]. (3) The Re-ID backbones include ResNet=50, ResNet-101, DenseNet-161 and DenseNet-201
• Approach 2 - The solution is decomposed into two stages - the first stage is to retrieve faces, and the second stage is to retrieve the bodies. Finally, the retrieval results of the two stages are combined as the ranking result. (1) Face Detection. The face detector used here are PCN [29] and MTCNN [26]. (2) Face Retrieval. A second-order networks [30], [31], [32] (ResNet34 as backbone) trained on VGGFace2 [33] with softmax loss and ring loss [34] is used here
• Approach 3 -  In the first step, the face in the query is used to search persons, whose faces can be detected, by face recognition. Then these images are further used to search again in all candidate images by person reidentification feature to get the final result

Happy Learning!!!

Day #296 - TensorflowLite

TensorflowLite models run in gmail, google photos, google assistant, google photos etc..

Advantages

• Low Latency
• No Data connection required
• On device sensors

Key Points

• On Device ML on many platforms
• Tensorflow model saved in graph format
• Converted to Lite format

TFLite

• Model compression
• Quantization
• Optimized SIMD Kernels
• Converter to generate model. Interpreter to run models

Benefits

• Cross-Platform deployment
• Inference speed increases
• Binary size reduction
• Hardware acceleration roadmap

#Build and save keras model
model = build_your_model()
tf.keras.experimental.export_saved_model(model,saved_model_dir)

#convert keras to tensorflow lite model
converter = tf.lite.TFLiteConverter.from_saved_models(saved_model_dir)
#To experiment new feature
converter.experimental_new_converter = True
tflite_model = converter.convert()

Link1, Link2

Improve Performance of models

• Reduce precision of weights (16 bit instead of 32 bit precision) - Quantization
• Pruning - Remove connections during training
• Op Kernels - ARM
• Delegates - GPU (Run on Specialized hardware)

Tensorflow Lite on Micro-controllers is an impressive move. More than mobiles, cross platforms this is a very impressive step.

Happy Learning!!!

Getting new ideas perspectives

Some meaningful tips for new ideas, solutions, creative thinking

mt: @Fisher85M
CC @MikeQuindazzi @antgrasso

What is involved with the #Lean process? [Infographic]#sixsigma #startups #GrowthHacking #education #UX #brainstorming #innovation #agile #CX #IoT

[via @AXA] @ipfconline1 @Fisher85M pic.twitter.com/5LoMRrRKPz

— C-Suite Tech Point (@CsuiteTechPoint) May 28, 2020

Happy Learning!!!

Day #295 - Age - Emotion - Gender Detection Model

It seems I am aging faster than ever.

Deep Learning Model for Age, Gender, Emotion and Real-time implementation. It seems I am agining faster. If it says I am 40, I have just a decade left to code and transition to something else.

Years progressed so fast seems already aged. I hope to code and do something till my day of death. Keep Going...

Happy Learning!!!

Day #294 - Setting up Home Surveillance System

Finally, after a few months, I was able to set up a Home Surveillance System. Person Detection and Real-time alert.

Installation

Demo

Happy Learning!!!

Day #293 - Date with RASA - Chatbot Learning day :)

Found an interesting workshop on end to end demo with RASA.

Training

Demo

Happy Learning!!!