100DaysOfMLCode

<blockquote class="twitter-tweet" data-lang="en">Who’s ready to take the 100 days of ML code challenge? That means coding machine learning for at least an hour everyday for the next 100 days. Pledge with the <a href="https://twitter.com/hashtag/100DaysOfMLCode?src=hash&ref_src=twsrc%5Etfw">#100DaysOfMLCode</a> hashtag, I’ll give the first few winners a shoutout!— Siraj Raval (@sirajraval) <a href="https://twitter.com/sirajraval/status/1014758160572141568?ref_src=twsrc%5Etfw">July 5, 2018</a></blockquote>

The above tweet explains how this repo started. This will be the journal for 100DaysOfMLCode pledge. I will add the progress I make everyday and will publish the code at regular intervals once I make enough progress with some task/problem.

| Day | Task | Updates |
| ------------- |:-------------| :---------------- |
| Day 1 | Object Detection-Windows 10 |* Set up the environment on Windows 10(no GPU) * Used TensorFlow and pre-trained SSDMobileNet * Compared results with Yolo object detection |
| Day 2 | Object Detection-Ubuntu |* Set up the environment on Ubuntu(no GPU) * Used TensorFlow and pre-trained FasterRCNN model * Compared SSDLite with FasterRCNN |
| Day 3 | Real Time Object Detection |* Installed opencv * Used TensorFlow and pre-trained SSDlite model * Tried object detection on live camera feed |
| Day 4 | Object Detection using Google Colab |* Setup Colab for object detection * Added comments and explanation for readability * Used same code and instructions as Linux * Added code to upload a new image for testing |
| Day 5 | Create dataset for gun detection |* Collected few hundred images of guns for retraining the model * Found a relevant dataset * Collected few more images from Internet * Found a handy tool for downloading images |
| Day 6 | Write a blog |* Attended Uber Maps Tech Talk * There were 2 talks * Maps is a very interesting area to work on * Wrote a detailed blog|
| Day 7 | Annotate the dataset |* Used LabelImage for annotating the images * Each image should be separately annotated * Attended Actions on Google event |
| Day 8 | Model training |* Made some required configuration changes for training * Faced few issues which I was able to resolve using GitHub Issues * Reducing the batch size fixed OOM error|
| Day 9 | Continue model training |* Started the training again today * Been more than 5 hours so far and the loss has come down to 1.2 * I used the saved checkpoint to try object detection, it didnt work * Training needs patience!|
| Day 10 | More model training |* Left the model to train since yesterday * It has been training close to 30 hours!! * The model gave bad results * Pre-trained models are handy and helpful. But it doesn't mean we can settle with a specific one!|
| Day 11 | Finally some gun detection |* Training is much faster on Colab * While each training step took 20-30secs on local CPU, on Colab it was 2-3 seconds * Initial loss with ssd model was close to 30 * Using faster rcnn, initial loss was 3 |
| Day 12 | Train using Faster RCNN |* Collected more images from IMFDB * Used 250 images for training * Took 20mins for the loss to reduce to 0.4 |
| Day 13 | Set up Raspberry Pi |* Recently got a Raspberry Pi 3 Model B+ * Setting up Raspberry Pi was easy * Installed Raspbian OS * VNC-Viewer is a handy tool for desktop sharing |
| Day 14 | Object Detection on Raspberry Pi |* Steps followed for Ubuntu helped a bit * Installed TensorFlow and skipped Protobuf * Used generated protobuf files from Ubuntu * Faced issues, Stack Overflow was helpful |
| Day 15 | Real Time Object Detection on Raspberry Pi |* Used OpenCV for connecting to the USB camera * There was some delay in the inference * Because number of frames per second was close to 0.8 * Using Pi camera instead of a USB camera might improve fps rate |
| Day 16 | TensorFlow Model Zoo |* TensorFlow Model Zoo is a repository of many pre-trained models * ssd_mobilenet_v1_ppn_coco is based on Pooling Pyramid Network architecture * PPN is a new variant of Single Shot Detector * This pre-trained model is more than 3x times smaller in size when compared to ssd_mobilenet|
| Day 17 | Read about Face Processing |* Shape of the face, Albedo and reflectance are three major components of face processing * Albedo map captures variations in skin pigmentation * In case of unconstrained environment, a lot of external factors are dynamic * In constrained environment, there will be some agreed upon standards |
| Day 18 | DeepDream and Neural Network Interpretation |* DeepDream is a program that takes an image as an input and gives another image as an output * Ouput images look dream-like/artistic/unclear/random * Understanding how each of the layers interpret and learn the patterns, will help in making the models better |
| Day 19 | Research on Face Editing and Processing using Deep Learning |* Deep Learning models can be used for face editing and processing * Series of effects need to be applied when regular editing tools are used * There are DL models which can perform some of those tasks quickly |
| Day 20 | Semantic Image Segmentaion |* Semantic Segmentation segments each object * Instead of a bounding box, colored segments represent the objects * Used DeepLab Model from TensorFlow |
| Day 21 | Kaggle Ship Detection Challenge, Run Length Encode, Blender |* Airbus started Ship Detection Challenge on Kaggle * Challenge is to detect ships from satellite images * Came to know what Run Length Encode is * Installed Blender |
| Day 22 | Blender |* Blender is open source and easy to use * Many resources to get started and a big community * BlenderGuru is a very useful resource |
| Day 23 | Blender Fundamentals|* Finished Blender Fundamentals Tutorial * Recommend using a mouse with middle button and a keyboard with Numpad * Extrude and Edge loops are cool! |
| Day 24 | Understanding Physics in Face Reconstruction |* Physically based rendering (PBR) refers to the concept of using realistic shading/lighting models * Shader is the algorithm for calculating the Color of each pixel * A texture is just a standard bitmap image that is applied over the mesh surface! |
| Day 25 | SfSNet Architecture |* Training is done using an encoder-decoder network * SfSnet is used for image decompostion and reconstruction * Network is more than 80 layers deep | | Day 26 | What are Normal Maps? |* Normal maps add more details to object surfaces * Gives an illusion of depth/height on the surface * Help in figuring out the geometry/shape of the scene/object |
| Day 27 | What are Albedo Maps? |* Best to remove highlights/shadows from texture maps * Using albedo maps highlights, shadows and light patches are removed * This gives an even/homogeneous layer with only the color component |
| Day 28 | Skin Reflectance and BRDF |* Skin reflectance is a complex process * Depends on factors like skin color, blood flow beneath the skin etc * Reflectance too contributes to variable face appearances and face encoding |
| Day 29 | Compact Pseudocode for SfSNet |* SfSNet uses two separate networks * Encoder-Decoder Network is used for obtaining real face data components * SfSNet with residual blocks is used for image decomposition and reconstruction |
| Day 30 | Encoder-Decoder and Autoencoder Networks |* Encoder-Decoder networks are good at feature representation * Autoencoders are variants of Encoder-Decoder networks

100DaysOfMLCode

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100DaysOfMLCode