add to transformers

README.md

@@ -26,6 +26,12 @@ in terms of top-1 accuracy, efficiency, and robustness on my dataset and [CMAD b
 
 <br>
 
+### Load 1000 Class PyTorch Jit Model
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("FredZhang7/efficientnetv2.5_rw_s", trust_remote_code=True)
+```
+
 ### Prepare Model for Training
 To change the number of classes, replace the linear classification layer. 
 Here's an example of how to convert the architecture into a trainable model.
@@ -69,8 +75,8 @@ I finetuned the existing models on either 299x299, 304x304, 320x320, or 384x384
 
 `efficientnet_b3_pruned` achieved the second highest top-1 accuracy as well as the highest epoch-1 training accuracy on my task, out of EfficientNetV2.5 small and all existing EfficientNet models my 24 GB VRAM RTX 3090 could handle.
 
-I will publish the detailed report in another model repository, including the link to the GVNS benchmarks. 
-This repository is only for the base model, pretrained on ImageNet, not my task.
+I will publish the detailed report in [this model repository](https://huggingface.co/aistrova/safesearch-v5.0).
+This repository is only for the base model, pretrained a bit on ImageNet, not my task.
 
 ### Carbon Emissions
-Comparing all models and testing my new architectures costed roughly 504 GPU hours, over a span of 27 days.
+Comparing all models and testing my new architectures costed roughly 648 GPU hours, over a span of 35 days.