Add readme

README.md

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+## Piggyback: https://arxiv.org/abs/1801.06519
+
+Pretrained masks and backbones are available here: 
+Datasets in PyTorch format are available here: https://uofi.box.com/s/ixncr3d85guosajywhf7yridszzg5zsq 
+The PyTorch-friendly Places365 dataset can be downloaded from http://places2.csail.mit.edu/download.html 
+Place masks in `checkpoints/` and unzipped datasets in `data/`
+
+|               |    VGG-16    |   VGG-16 BN  |   ResNet-50  | DenseNet-121 |
+|:-------------:|:------------:|:------------:|:------------:|:------------:|
+| CUBS          |              |              |              |              |
+| Stanford Cars |              |              |              |              |
+| Flowers       |              |              |              |              |
+| WikiArt       |              |              |              |              |
+| Sketch        |              |              |              |              |
+
+Note that the numbers in the [paper](https://arxiv.org/abs/1801.06519) are averaged over multiple runs for each ordering
+of datasets. 
+These numbers were obtained by evaluating the models on a Titan X (Pascal). 
+Note that numbers on other GPUs might be slightly different (~0.1%) owing to cudnn algorithm selection. 
+https://discuss.pytorch.org/t/slightly-different-results-on-k-40-v-s-titan-x/10064
+
+## Requirements:
+torch==0.2.0.post3
+torchvision==0.1.9
+
+
+Run all code from the `src/` directory, e.g. `./scripts/run_piggyback_training.sh`
+
+## Training:
+Check out `src/scripts/run_piggyback_training.sh`.
+This script uses the default hyperparams and trains a model as described in the paper. The best performing model on the val set
+is saved to disk. This saved model includes the real-valued mask weights.
+
+## Saving trained masks only.
+Check out `src/scripts/run_packing.sh`.
+This extracts the binary/ternary masks from the above trained models, and saves them separately.
+
+## Eval:
+Use the saved masks, apply them to a backbone network and run eval.
+By default, our backbone models are those provided with pytorch.
+Note that to replicate our results, you have to use the package versions specified above. 
+Newer package versions might have different weights for the backbones, and the provided masks won't work.
+```bash
+cd src  # Run everything from src/
+
+CUDA_VISIBLE_DEVICES=0 python pack.py --mode eval --dataset flowers \
+  --arch vgg16 \
+  --maskloc ../checkpoints/vgg16_binary.pt
+```