v0.51.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.51.0 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/ibaiGorordo/CREStereo-Pytorch/blob/main/LICENSE.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- android
+pipeline_tag: depth-estimation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/web-assets/model_demo.png)
+
+# CREStereo: Optimized for Qualcomm Devices
+
+CREStereo (Cascaded Recurrent Network with Adaptive Correlation) is a CVPR 2022 Oral paper that achieves state-of-the-art stereo matching accuracy.
+
+This is based on the implementation of CREStereo found [here](https://github.com/ibaiGorordo/CREStereo-Pytorch).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/crestereo) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+
+Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
+
+## Getting Started
+There are two ways to deploy this model on your device:
+
+### Option 1: Download Pre-Exported Models
+
+Below are pre-exported model assets ready for deployment.
+
+| Runtime | Precision | Chipset | SDK Versions | Download |
+|---|---|---|---|---|
+| ONNX | float | Universal | ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-qnn_dlc-float.zip)
+| TFLITE | float | Universal |  | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-tflite-float.zip)
+
+For more device-specific assets and performance metrics, visit **[CREStereo on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/crestereo)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/crestereo) Python library to compile and export the model with your own:
+- Custom weights (e.g., fine-tuned checkpoints)
+- Custom input shapes
+- Target device and runtime configurations
+
+This option is ideal if you need to customize the model beyond the default configuration provided here.
+
+See our repository for [CREStereo on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/crestereo) for usage instructions.
+
+## Model Details
+
+**Model Type:** Model_use_case.depth_estimation
+
+**Model Stats:**
+- Model checkpoint: CREStereo ETH3D pretrained (crestereo_eth3d.pt)
+- Input: Rectified stereo pair — left and right RGB images
+- Input resolution: 240x320
+- Output: Disparity map
+- Number of parameters: 5.43M
+- Model size (float): 20.7 MB
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| CREStereo | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 3841.905 ms | 77 - 97 MB | CPU
+| CREStereo | ONNX | float | Snapdragon® X2 Elite | 2034.246 ms | 179 - 179 MB | CPU
+| CREStereo | ONNX | float | Snapdragon® X Elite | 6292.226 ms | 184 - 184 MB | CPU
+| CREStereo | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4583.867 ms | 59 - 81 MB | CPU
+| CREStereo | ONNX | float | Qualcomm® QCS8550 (Proxy) | 5846.538 ms | 65 - 68 MB | CPU
+| CREStereo | ONNX | float | Qualcomm® QCS9075 | 3946.878 ms | 95 - 97 MB | CPU
+| CREStereo | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3982.126 ms | 62 - 85 MB | CPU
+| CREStereo | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 2891.856 ms | 143 - 233 MB | CPU
+| CREStereo | QNN_DLC | float | Snapdragon® X2 Elite | 2830.939 ms | 85 - 85 MB | CPU
+| CREStereo | QNN_DLC | float | Snapdragon® X Elite | 9926.739 ms | 85 - 85 MB | CPU
+| CREStereo | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 4485.42 ms | 120 - 212 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 10840.625 ms | 131 - 216 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 5622.908 ms | 111 - 154 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® SA8775P | 6649.979 ms | 129 - 213 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® QCS9075 | 7805.077 ms | 304 - 1206 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 7283.514 ms | 141 - 236 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® SA7255P | 10840.625 ms | 131 - 216 MB | CPU
+| CREStereo | QNN_DLC | float | Qualcomm® SA8295P | 5593.28 ms | 129 - 213 MB | CPU
+| CREStereo | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 3425.318 ms | 125 - 212 MB | CPU
+| CREStereo | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2917.477 ms | 45 - 73 MB | CPU
+| CREStereo | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4461.271 ms | 46 - 76 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 7591.053 ms | 48 - 73 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 5318.402 ms | 47 - 60 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® SA8775P | 6053.307 ms | 48 - 73 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® QCS9075 | 6158.679 ms | 47 - 153 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 7052.932 ms | 48 - 81 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® SA7255P | 7591.053 ms | 48 - 73 MB | CPU
+| CREStereo | TFLITE | float | Qualcomm® SA8295P | 4082.577 ms | 47 - 73 MB | CPU
+| CREStereo | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3275.879 ms | 47 - 75 MB | CPU
+
+## License
+* The license for the original implementation of CREStereo can be found
+  [here](https://github.com/ibaiGorordo/CREStereo-Pytorch/blob/main/LICENSE).
+
+## References
+* [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://arxiv.org/abs/2203.11483)
+* [Source Model Implementation](https://github.com/ibaiGorordo/CREStereo-Pytorch)
+
+## 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).

release_assets.json

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+{
+    "version": "0.51.0",
+    "precisions": {
+        "float": {
+            "universal_assets": {
+                "tflite": {
+                    "tool_versions": {
+                        "litert": "1.4.2"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-tflite-float.zip"
+                },
+                "qnn_dlc": {
+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-qnn_dlc-float.zip"
+                },
+                "onnx": {
+                    "tool_versions": {
+                        "onnx_runtime": "1.24.3"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/crestereo/releases/v0.51.0/crestereo-onnx-float.zip"
+                }
+            }
+        }
+    }
+}