Update README.md

README.md

@@ -102,7 +102,7 @@ For an image resolution of NxM and P classes
 * The deployment of all the models listed in the table is supported, except for the efficientnet_v2S_384 model, for which support is coming soon.
 ### Accuracy with Food-101 dataset
 
-Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) , License [-](), Quotation[[3]](#3)  , Number of classes: 101 , Number of images:  101 000
+Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) , Quotation[[3]](#3)  , Number of classes: 101 , Number of images:  101 000
 
 | Model                                                                                                                                            | Format | Resolution | Top 1 Accuracy |
 |--------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|----------------|
@@ -118,7 +118,7 @@ Dataset details: [link](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-1
 
 ### Accuracy with ImageNet
 
-Dataset details: [link](https://www.image-net.org), License: BSD-3-Clause, Quotation[[4]](#4)
+Dataset details: [link](https://www.image-net.org), Quotation[[4]](#4)
 Number of classes: 1000.
 To perform the quantization, we calibrated the activations with a random subset of the training set.
 For the sake of simplicity, the accuracy reported here was estimated on the 10000 labelled images of the validation set.