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README.md

@@ -18,7 +18,7 @@ tags:
 
 VIT is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
 
-This model is an implementation of VIT found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py).
+This model is an implementation of VIT found [here]({source_repo}).
 This repository provides scripts to run VIT on Qualcomm® devices.
 More details on model performance across various devices, can be found
 [here](https://aihub.qualcomm.com/models/vit).
@@ -33,14 +33,23 @@ More details on model performance across various devices, can be found
   - Number of parameters: 86.6M
   - Model size: 330 MB
 
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| VIT | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 19.821 ms | 0 - 3 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 15.505 ms | 0 - 193 MB | FP16 | NPU | [VIT.onnx](https://huggingface.co/qualcomm/VIT/blob/main/VIT.onnx) |
+| VIT | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 16.903 ms | 0 - 382 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 11.372 ms | 0 - 149 MB | FP16 | NPU | [VIT.onnx](https://huggingface.co/qualcomm/VIT/blob/main/VIT.onnx) |
+| VIT | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 19.788 ms | 0 - 3 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 19.83 ms | 0 - 3 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 20.031 ms | 0 - 3 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 20.358 ms | 0 - 3 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 24.972 ms | 0 - 368 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 11.489 ms | 0 - 207 MB | FP16 | NPU | [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) |
+| VIT | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 9.01 ms | 1 - 112 MB | FP16 | NPU | [VIT.onnx](https://huggingface.co/qualcomm/VIT/blob/main/VIT.onnx) |
+| VIT | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 21.624 ms | 171 - 171 MB | FP16 | NPU | [VIT.onnx](https://huggingface.co/qualcomm/VIT/blob/main/VIT.onnx) |
 
 
 
-| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
-| ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 19.822 ms | 0 - 3 MB | FP16 | NPU |  [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) 
-
-
 
 ## Installation
 
@@ -95,7 +104,17 @@ device. This script does the following:
 ```bash
 python -m qai_hub_models.models.vit.export
 ```
-
+```
+Profiling Results
+------------------------------------------------------------
+VIT
+Device                          : Samsung Galaxy S23 (13)
+Runtime                         : TFLITE                 
+Estimated inference time (ms)   : 19.8                   
+Estimated peak memory usage (MB): [0, 3]                 
+Total # Ops                     : 1579                   
+Compute Unit(s)                 : NPU (1579 ops)         
+```
 
 
 ## How does this work?
@@ -193,15 +212,19 @@ provides instructions on how to use the `.so` shared library  in an Android appl
 Get more details on VIT's performance across various devices [here](https://aihub.qualcomm.com/models/vit).
 Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
+
 ## License
-- The license for the original implementation of VIT can be found
-  [here](https://github.com/pytorch/vision/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
+* The license for the original implementation of VIT can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
+
+
 
 ## References
 * [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)
 * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py)
 
+
+
 ## Community
 * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).