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also use google drive for model zoo and calib data

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wang-xinyu 2021-03-08 18:44:50 +08:00
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README.md
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@ -87,7 +87,7 @@ Following models are implemented.
The .wts files can be downloaded from model zoo for quick evaluation. But it is recommended to convert .wts from pytorch/mxnet/tensorflow model, so that you can retrain your own model.
[BaiduPan](https://pan.baidu.com/s/19s6hO8esU7-TtZEXN7G3OA) pwd: uvv2
[GoogleDrive](https://drive.google.com/drive/folders/1Ri0IDa5OChtcA3zjqRTW57uG6TnfN4Do?usp=sharing) | [BaiduPan](https://pan.baidu.com/s/19s6hO8esU7-TtZEXN7G3OA) pwd: uvv2
## Tricky Operations

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retinaface/README.md
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@ -46,7 +46,7 @@ sudo ./retina_r50 -d // deserialize model file and run inference.
# INT8 Quantization
1. Prepare calibration images, you can randomly select 1000s images from your train set. For widerface, you can also download my calibration images `widerface_calib` from [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
1. Prepare calibration images, you can randomly select 1000s images from your train set. For widerface, you can also download my calibration images `widerface_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
2. unzip it in retinaface/build

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yolov3/README.md
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@ -45,7 +45,7 @@ sudo ./yolov3 -d ../../yolov3-spp/samples // deserialize plan file and run infer
# INT8 Quantization
1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
2. unzip it in yolov3/build

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yolov5/README.md
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@ -69,7 +69,7 @@ python yolov5_trt.py
# INT8 Quantization
1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
2. unzip it in yolov5/build