Update README.md

README.md

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 We offer a TensorRT model in various precisions including int8, fp16, fp32, and mixed, converted from Deci-AI's [YOLO-NAS-Pose](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md) pre-trained weights in PyTorch. 
 This model is compatible with Jetson Orin Nano hardware. 
 
-Note that all quantization that has been introduced in the conversion is purely static, meaning that the corresponding model has potentillay bad accuracy compared to the original one.
+~~Note that all quantization that has been introduced in the conversion is purely static, meaning that the corresponding model has potentillay bad accuracy compared to the original one.~~
 
-Todo: use [coco-pose-2017](https://huggingface.co/datasets/Mai0313/coco-pose-2017) dataset to calibrate int8 model
+Todo: ~~use [cppe-5](https://huggingface.co/datasets/cppe-5) dataset to calibrate int8 model~~
 
 More information on calibration for post-training quantization, check [this slide](https://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf)