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fix model to fit the new contrib structure, fix links
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zhreshold committed Mar 23, 2017
1 parent d7f8cd3 commit 17b6aac
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4 changes: 2 additions & 2 deletions example/ssd/README.md
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Expand Up @@ -54,7 +54,7 @@ Remember to enable CUDA if you want to be able to train, since CPU training is
insanely slow. Using CUDNN is optional.

### Try the demo
* Download the pretrained model: [`ssd_300.zip`](https://dl.dropboxusercontent.com/u/39265872/ssd_300_vgg16_reduced_voc0712_trainval.zip), and extract to `model/` directory. (This model is converted from VGG_VOC0712_SSD_300x300_iter_60000.caffemodel provided by paper author).
* Download the pretrained model: [`ssd_300.zip`](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.2-alpha/ssd_300_vgg16_reduced_voc0712_trainval.zip), and extract to `model/` directory. (This model is converted from VGG_VOC0712_SSD_300x300_iter_60000.caffemodel provided by paper author).
* Run
```
# cd /path/to/mxnet/example/ssd/
Expand All @@ -71,7 +71,7 @@ python demo.py --epoch 0 --images ./data/demo/dog.jpg --thresh 0.5
This example only covers training on Pascal VOC dataset. Other datasets should
be easily supported by adding subclass derived from class `Imdb` in `dataset/imdb.py`.
See example of `dataset/pascal_voc.py` for details.
* Download the converted pretrained `vgg16_reduced` model [here](https://dl.dropboxusercontent.com/u/39265872/vgg16_reduced.zip), unzip `.param` and `.json` files
* Download the converted pretrained `vgg16_reduced` model [here](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.2-beta/vgg16_reduced.zip), unzip `.param` and `.json` files
into `model/` directory by default.
* Download the PASCAL VOC dataset, skip this step if you already have one.
```
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2 changes: 1 addition & 1 deletion example/ssd/detect/detector.py
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Expand Up @@ -32,7 +32,7 @@ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \
if self.ctx is None:
self.ctx = mx.cpu()
_, args, auxs = mx.model.load_checkpoint(model_prefix, epoch)
self.mod = mx.mod.Module(symbol, context=ctx)
self.mod = mx.mod.Module(symbol, label_names=None, context=ctx)
self.data_shape = data_shape
self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))])
self.mod.set_params(args, auxs)
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