updating README with images and more links

README.md

@@ -1,17 +1,34 @@
 # PlantNet-300K
 
+<p align="middle">
+  <img src="/images/1.jpg" width="180" hspace="2"/>
+  <img src="/images/2.jpg" width="180" hspace="2"/>
+  <img src="/images/3.jpg" width="180" hspace="2"/>
+  <img src="/images/4.jpg" width="180" hspace="2"/>
+</p>
+
 This repository contains the code used to produce the benchmark in the paper *"Pl@ntNet-300K: a plant image dataset with high label
 ambiguity and a long-tailed distribution"*. You can find a link to the paper [here](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7e7757b1e12abcb736ab9a754ffb617a-Paper-round2.pdf).
-In order to train a model on the PlantNet-300K dataset, you first have to download the dataset [here](https://doi.org/10.5281/zenodo.4726653).
+In order to train a model on the PlantNet-300K dataset, you first have to download the dataset [here](https://zenodo.org/record/5645731#.Yuehg3ZBxPY).
 
 If you use this work for this research, please cite the paper :
 
-    @inproceedings{garcin2021pl,
-      title={Pl@ ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution},
-      author={Garcin, Camille and Joly, Alexis and Bonnet, Pierre and Lombardo, Jean-Christophe and Affouard, Antoine and Chouet, Mathias and Servajean, Maximilien and Salmon, Joseph and Lorieul, Titouan},
-      booktitle={NeurIPS 2021-35th Conference on Neural Information Processing Systems},
-      year={2021}
+    @inproceedings{plantnet-300k,
+    author    = {C. Garcin and A. Joly and P. Bonnet and A. Affouard and \JC Lombardo and M. Chouet and M. Servajean and T. Lorieul and J. Salmon},
+    booktitle = {NeurIPS Datasets and Benchmarks 2021},
+    title     = {{Pl@ntNet-300K}: a plant image dataset with high label ambiguity and a long-tailed distribution},
+    year      = {2021},
     }
+    
+### Dataset Version // Meta-data files
+
+Make sure you download the latest version of the dataset in Zenoodo (version 1.1 as in the link above, not 1.0).
+The difference lies in the metadata files, the images are the same.
+If you wish to download **ONLY** the metadata files (not possible in Zenodo), you will find them [here](https://lab.plantnet.org/seafile/d/bed81bc15e8944969cf6/).
+
+### Pre-trained models
+
+You can find the pre-trained models [here](https://lab.plantnet.org/seafile/d/01ab6658dad6447c95ae/).
 
 ### Requirements
 
@@ -24,7 +41,7 @@ If you have installed anaconda, you can run the following command :
 
 In order to train a model on the PlantNet-300K dataset, run the following command :
 
-```python main.py --lr=0.05 --n_epochs=80 --k 1 3 5 10 --model=resnet50 --root=path_to_data --save_name_xp=xp1```
+```python main.py --lr=0.01 --batch_size=32 --mu=0.0001 --n_epochs=30 --epoch_decay 20 25 --k 1 3 5 10 --model=resnet18 --pretrained --seed=4 --image_size=256 --crop_size=224 --root=path_to_data --save_name_xp=xp1```
 
  You must provide in the "root" option the path to the train val and test folders. 
  The "save_name_xp" option is the name of the directory where the weights of the model and the results (metrics) will be stored.