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42 changes: 23 additions & 19 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,9 @@

Real-time object detection and classification. Paper: [version 1](https://arxiv.org/pdf/1506.02640.pdf), [version 2](https://arxiv.org/pdf/1612.08242.pdf).

Read more about YOLO (in darknet) and download weight files [here](http://pjreddie.com/darknet/yolo/). In case the weight file cannot be found, I uploaded some of mine [here](https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU), which include `yolo-full` and `yolo-tiny` of v1.0, `tiny-yolo-v1.1` of v1.1 and `yolo`, `tiny-yolo-voc` of v2.
Read more about YOLO (in darknet) and download weight files [here](https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU), which include `yolo-full` and `yolo-tiny` of v1.0, `tiny-yolo-v1.1` of v1.1 and `yolo`, `tiny-yolo-voc` of v2.

Store the weights in a folder `bin\`.

See demo below or see on [this imgur](http://i.imgur.com/EyZZKAA.gif)

Expand All @@ -21,7 +22,7 @@ You can choose _one_ of the following three ways to get started with darkflow.

1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use `./flow` in the cloned darkflow directory instead of `flow` as darkflow is not installed globally.
```
python3 setup.py build_ext --inplace
python setup.py build_ext --inplace
```

2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
Expand Down Expand Up @@ -82,33 +83,36 @@ activation = linear

```bash
# Have a look at its options
# On Windows
python flow --h
# Others
flow --h
```

First, let's take a closer look at one of a very useful option `--load`

```bash
# 1. Load tiny-yolo.weights
flow --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
python flow --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights

# 2. To completely initialize a model, leave the --load option
flow --model cfg/yolo-new.cfg
python flow --model cfg/yolo-new.cfg

# 3. It is useful to reuse the first identical layers of tiny for `yolo-new`
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights
python flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights
# this will print out which layers are reused, which are initialized
```

All input images from default folder `sample_img/` are flowed through the net and predictions are put in `sample_img/out/`. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.

```bash
# Forward all images in sample_img/ using tiny yolo and 100% GPU usage
flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --gpu 1.0
python flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --gpu 1.0
```
json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the `sample_img/out` folder by default. An example json array is shown below.
```bash
# Forward all images in sample_img/ using tiny yolo and JSON output.
flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --json
python flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --json
```
JSON output:
```json
Expand All @@ -127,23 +131,23 @@ Training is simple as you only have to add option `--train`. Training set and an

```bash
# Initialize yolo-new from yolo-tiny, then train the net on 100% GPU:
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights --train --gpu 1.0
python flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights --train --gpu 1.0

# Completely initialize yolo-new and train it with ADAM optimizer
flow --model cfg/yolo-new.cfg --train --trainer adam
python flow --model cfg/yolo-new.cfg --train --trainer adam
```

During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in `ckpt/`. To resume to any checkpoint before performing training/testing, use `--load [checkpoint_num]` option, if `checkpoint_num < 0`, `darkflow` will load the most recent save by parsing `ckpt/checkpoint`.

```bash
# Resume the most recent checkpoint for training
flow --train --model cfg/yolo-new.cfg --load -1
python flow --train --model cfg/yolo-new.cfg --load -1

# Test with checkpoint at step 1500
flow --model cfg/yolo-new.cfg --load 1500
python flow --model cfg/yolo-new.cfg --load 1500

# Fine tuning yolo-tiny from the original one
flow --train --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
python flow --train --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
```

Example of training on Pascal VOC 2007:
Expand All @@ -156,7 +160,7 @@ tar xf VOCtest_06-Nov-2007.tar
vim VOCdevkit/VOC2007/Annotations/000001.xml

# Train the net on the Pascal dataset:
flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages" --annotation "~/VOCdevkit/VOC2007/Annotations"
python flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages" --annotation "~/VOCdevkit/VOC2007/Annotations"
```

### Training on your own dataset
Expand Down Expand Up @@ -208,7 +212,7 @@ flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages"
```
5. Reference the `tiny-yolo-voc-3c.cfg` model when you train.

`flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images`
`python flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images`


* Why should I leave the original `tiny-yolo-voc.cfg` file unchanged?
Expand All @@ -221,13 +225,13 @@ flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages"
For a demo that entirely runs on the CPU:

```bash
flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi
python flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi
```

For a demo that runs 100% on the GPU:

```bash
flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi --gpu 1.0
python flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi --gpu 1.0
```

To use your webcam/camera, simply replace `videofile.avi` with keyword `camera`.
Expand Down Expand Up @@ -258,10 +262,10 @@ print(result)

```bash
## Saving the lastest checkpoint to protobuf file
flow --model cfg/yolo-new.cfg --load -1 --savepb
python flow --model cfg/yolo-new.cfg --load -1 --savepb

## Saving graph and weights to protobuf file
flow --model cfg/yolo.cfg --load bin/yolo.weights --savepb
python flow --model cfg/yolo.cfg --load bin/yolo.weights --savepb
```
When saving the `.pb` file, a `.meta` file will also be generated alongside it. This `.meta` file is a JSON dump of everything in the `meta` dictionary that contains information nessecary for post-processing such as `anchors` and `labels`. This way, everything you need to make predictions from the graph and do post processing is contained in those two files - no need to have the `.cfg` or any labels file tagging along.

Expand All @@ -270,7 +274,7 @@ The created `.pb` file can be used to migrate the graph to mobile devices (JAVA
Also, darkflow supports loading from a `.pb` and `.meta` file for generating predictions (instead of loading from a `.cfg` and checkpoint or `.weights`).
```bash
## Forward images in sample_img for predictions based on protobuf file
flow --pbLoad built_graph/yolo.pb --metaLoad built_graph/yolo.meta --imgdir sample_img/
python flow --pbLoad built_graph/yolo.pb --metaLoad built_graph/yolo.meta --imgdir sample_img/
```
If you'd like to load a `.pb` and `.meta` file when using `return_predict()` you can set the `"pbLoad"` and `"metaLoad"` options in place of the `"model"` and `"load"` options you would normally set.

Expand Down