Add vision tag

README.md

@@ -113,6 +113,33 @@ af = torch.mean(torch.stack([bella, sarah]), dim=0)
 assert torch.equal(af, torch.load('voices/af.pt', weights_only=True))
 ```
 
+### Pretraining
+
+For all pre-training related hyperparameters, we refer to page 15 of the [original paper](https://arxiv.org/abs/2106.08254).
+
+## Evaluation results
+
+For evaluation results on several image classification benchmarks, we refer to tables 1 and 2 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance.
+
+### BibTeX entry and citation info
+
+```@article{DBLP:journals/corr/abs-2106-08254,
+  author    = {Hangbo Bao and
+               Li Dong and
+               Furu Wei},
+  title     = {BEiT: {BERT} Pre-Training of Image Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2106.08254},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2106.08254},
+  archivePrefix = {arXiv},
+  eprint    = {2106.08254},
+  timestamp = {Tue, 29 Jun 2021 16:55:04 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08254.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
 ### Training Details
 
 **Compute:** Darwin-AI v0.19 was trained on A100 80GB vRAM instances for approximately 500 total GPU hours. The average cost for each GPU hour was around $0.80, so the total cost was around $400.