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Support SSD f32/int8 evaluation on COCO dataset #14646
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pengzhao-intel
approved these changes
Apr 9, 2019
example/ssd/README.md
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| @@ -119,7 +120,7 @@ You can use `./demo.py --camera` to use a video capture device with opencv such | |||
| will open a window that will display the camera output together with the detections. You can play | |||
| with the detection threshold to get more or less detections. | |||
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| ### Train the model | |||
| ### Train the model on VOC | |||
| * Note that we recommend to use gluon-cv to train the model, please refer to [gluon-cv ssd](https://gluon-cv.mxnet.io/build/examples_detection/train_ssd_voc.html). | |||
| This example only covers training on Pascal VOC dataset. Other datasets should | |||
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modify this sentence since we already support COCO now :)
| Follow the [Train instructions](https://github.com/apache/incubator-mxnet/tree/master/example/ssd#train-the-model) to train a FP32 `SSD-VGG16_reduced_300x300` model based on Pascal VOC dataset. You can also download our [SSD-VGG16 pre-trained model](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_vgg16_reduced_300-dd479559.zip) and [packed binary data](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/ssd-val-fc19a535.zip). Create `model` and `data` directories if they're not exist, extract the zip files, then rename the uncompressed files as follows (eg, rename `ssd-val-fc19a535.idx` to `val.idx`, `ssd-val-fc19a535.lst` to `val.lst`, `ssd-val-fc19a535.rec` to `val.rec`, `ssd_vgg16_reduced_300-dd479559.params` to `ssd_vgg16_reduced_300-0000.params`, `ssd_vgg16_reduced_300-symbol-dd479559.json` to `ssd_vgg16_reduced_300-symbol.json`.) | ||
| To quantize a model on VOC dataset, follow the [Train instructions](https://github.com/apache/incubator-mxnet/tree/master/example/ssd#train-the-model-on-VOC) to train a FP32 `SSD-VGG16_reduced_300x300` model based on Pascal VOC dataset. You can also download our [SSD-VGG16 pre-trained model](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_vgg16_reduced_300-dd479559.zip) and [packed binary data](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/ssd-val-fc19a535.zip). Create `model` and `data` directories if they're not exist, extract the zip files, then rename the uncompressed files as follows (eg, rename `ssd-val-fc19a535.idx` to `val.idx`, `ssd-val-fc19a535.lst` to `val.lst`, `ssd-val-fc19a535.rec` to `val.rec`, `ssd_vgg16_reduced_300-dd479559.params` to `ssd_vgg16_reduced_300-0000.params`, `ssd_vgg16_reduced_300-symbol-dd479559.json` to `ssd_vgg16_reduced_300-symbol.json`.) | ||
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| To quantize a model on COCO dataset, follow the [Train instructions](https://github.com/apache/incubator-mxnet/tree/master/example/ssd#train-the-model-on-COCO) to train a FP32 `SSD-VGG16_reduced_300x300` model based on COCO dataset. You can also download our [SSD-VGG16 pre-trained model](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_vgg16_reduced_300-7fedd4ad.zip) and [packed binary data](http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/ssd_coco-val-e91096e8.zip). Create `model` and `data` directories if they're not exist, extract the zip files, then rename the uncompressed files as follows (eg, rename `ssd_coco-val-e91096e8.idx` to `val.idx`, `ssd_coco-val-e91096e8.lst` to `val.lst`, `ssd_coco-val-e91096e8.rec` to `val.rec`, `ssd_vgg16_reduced_300-7fedd4ad.params` to `ssd_vgg16_reduced_300-0000.params`, `ssd_vgg16_reduced_300-symbol-7fedd4ad.json` to `ssd_vgg16_reduced_300-symbol.json`.) | ||
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what's the difference between two paragrams of VOC and COCO?
Could you merge into one?
zhreshold
approved these changes
Apr 9, 2019
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lgtm, will wait until ci passes |
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Thank you for your contribution. Merging now. |
haohuanw
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Jun 23, 2019
* support ssd coco * improve doc
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Description
This PR enable ssd training and f32/int8 evaluation on coco2014 dataset.
Changes:
@pengzhao-intel @TaoLv @zhreshold
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