Update README.md

README.md

@@ -73,7 +73,7 @@ You can find the final dataset splitted into train and test  [rootstrap-org/wast
 
 Split into train/test 
 You can find the code for the classifier [resnet-model.ipynb](https://github.com/rootstrap/fastai-waste-classifier/blob/main/resnet-model.ipynb)
-To load the model in the file [result-resnet50.pkl](result-resnet50.pkl)
+load the model [result-resnet50.pkl](https://huggingface.co/rootstrap-org/waste-classifier/blob/main/result-resnet50.pkl)
 
 The model has been trained to classify waste into 6 classes. 
 This repository contains the dataset that we collected. 
@@ -86,18 +86,18 @@ The dataset used for the first training consisted of 2527 images:
 - 137 trash
 
 The dataset was built joining images search on the internet using Bing plus some images taken manually, in addition to images from [Trashnet dataset](https://github.com/garythung/trashnet)
-If you are using the dataset, please give a citation of this repository. The dataset can be downloaded at [rootstrap-org/waste-classifier](rootstrap-org/waste-classifier).
+If you are using the dataset, please give a citation of this repository. The dataset can be downloaded at [rootstrap-org/waste-classifier](https://huggingface.co/datasets/rootstrap-org/waste-classifier).
 
-In the second training, another set of images was collected using the [TACO dataset](http://tacodataset.org/) as a starting point, along with other datasets in the web. A selection of proper training images was made from those datasets to build the training dataset.
+In the second training, another set of images was collected using the [TACO dataset](http://tacodataset.org/) as a starting point, along with other datasets in the web. 
+A selection of proper training images was made from those datasets to build the training dataset.
 
 ### Training Procedure 
 You can find the code for training at [resnet-model.ipynb](https://github.com/rootstrap/fastai-waste-classifier/blob/main/resnet-model.ipynb)
-Training the model using resnet50: ResNet34 is a convolutional neural network(CNN) that has 34 layers. It has been already trained with images from the ImageNet database. It classifies 1000 object from very broad categories, such as pencil or animals. The input size of the network is 224x224.
+Training the model by fine tuning resnet50, a convolutional neural network that has 50 layers. It is pretrained on ImageNet dataset and can classify images into 1000 object categories. 
+The expected input size is 224x224.
 
 ## Evaluation and Results
-After the first training, the model reached an accuracy of 0.98, here we can see the confusion matrix:
-![confusion_matrix](https://github.com/rootstrap/fastai-waste-classifier/blob/main/classification_matrix_resnet34.png)
+After the first training, the model reached an accuracy of 0.98.
 However, that first model had a lot of bias and limitations.
-
 The final model trained with more images, many of them from the TACO dataset, reached an accuracy of 89%.
 That model still has some limitation, but it's a lot more flexible than the first one.