Code for Keras plays catch blog post
python qlearn.py- Generate figures
kakittwo
vincentchu
/ ssd_full_infer.ipynb
Created
January 23, 2018 18:00
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fchollet
/ classifier_from_little_data_script_3.py
Last active
September 13, 2023 03:34
Fine-tuning a Keras model. Updated to the Keras 2.0 API.
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| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
karpathy
/ pg-pong.py
Created
May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
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| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
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| #import necessary libraries | |
| import cv2 | |
| import numpy as np | |
| #capture video from the webcam | |
| cap = cv2.VideoCapture(0) | |
| #load the face finder | |
| face_cascade = cv2.CascadeClassifier('/home/sm/Desktop/haarcascade_frontalface_default.xml') |
EderSantana
/ CATCH_Keras_RL.md
Last active
October 16, 2023 08:32
Keras plays catch - a single file Reinforcement Learning example
yrevar
/ imagenet1000_clsidx_to_labels.txt
Last active
April 29, 2024 06:47
text: imagenet 1000 class idx to human readable labels (Fox, E., & Guestrin, C. (n.d.). Coursera Machine Learning Specialization.)
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| {0: 'tench, Tinca tinca', | |
| 1: 'goldfish, Carassius auratus', | |
| 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
| 3: 'tiger shark, Galeocerdo cuvieri', | |
| 4: 'hammerhead, hammerhead shark', | |
| 5: 'electric ray, crampfish, numbfish, torpedo', | |
| 6: 'stingray', | |
| 7: 'cock', | |
| 8: 'hen', | |
| 9: 'ostrich, Struthio camelus', |
Probably the most straight forward way to start generating Point Clouds from a set of pictures.
VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.
For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]