uploaded files

.gitattributes

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+*.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+*.mp4
+

README.md

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+# Predict Car Speed from Dashcam video
+
+## Goal
+
+_Basically, the goal is to predict the speed of a car from a video._
+
+data/train.mp4 is a video of driving containing 20400 frames. Video is shot at 20 fps.
+data/train.txt contains the speed of the car at each frame, one speed on each line.
+
+data/test.mp4 is a different driving video containing 10798 frames. Video is shot at 20 fps.
+The deliverable is test.txt
+
+## Usage:  
+#### For Train
+```bash
+python main.py data/train.mp4 data/train.txt --mode=train --split=0.3
+```
+If you'd like to continue training using the pretrained network then add the `--resume` flag to that line. <br>
+If any modifications are made to the optical flow part of the model then `--wipe` must be used to reprocess the data
+
+#### For Evaluate
+```bash
+python main.py data/train.mp4 data/train.txt --mode=eval
+```
+This will print the mean squared error.
+
+#### For Play
+```bash
+python main.py data/train.mp4 data/train.txt --mode=play
+```
+If you want a more graphical display you can use the play mode. This will output the Optical Flow video with prediction overlay.
+
+#### For Test/Inference
+```bash
+python main.py data/test.mp4 data/test.txt --mode=test
+```
+It will infer the model and save the predicted value to test.txt file.
+
+## Results
+I divided flow_data in 70-30 ratio in train and validation set. <br>
+I got a MSE of around 2.5 on train data and 0.45 on validation data. MSE on entire train data is **0.55**.
+
+#### Visualization of Results
+![output1](results/output.gif) <br>
+
+![output2](results/output2.gif)

data/evaluate_mse.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[9]:
+
+
+import numpy as np
+import os
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+
+
+# In[10]:
+
+
+y_true = np.array(pd.read_csv("train.txt", header=None, squeeze=True))
+y_pred = np.array(pd.read_csv("test.txt", header=None, squeeze=True))
+
+
+# In[11]:
+
+
+mean_squared_error(y_true, y_pred)
+
+
+# In[ ]:
+
+
+
+