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Conditional-DETR-ResNet50

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v0.46.1

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See https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.

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  1. Conditional-DETR-ResNet50_float.dlc +0 -3
  2. Conditional-DETR-ResNet50_float.onnx.zip +0 -3
  3. Conditional-DETR-ResNet50_float.tflite +0 -3
  4. README.md +73 -227
  5. tool-versions.yaml +0 -4
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  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/web-assets/model_demo.png)
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- # Conditional-DETR-ResNet50: Optimized for Mobile Deployment
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- ## Transformer based object detector with ResNet50 backbone
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-
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  DETR is a machine learning model that can detect objects (trained on COCO dataset).
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- This model is an implementation of Conditional-DETR-ResNet50 found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr).
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-
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-
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- This repository provides scripts to run Conditional-DETR-ResNet50 on Qualcomm® devices.
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- More details on model performance across various devices, can be found
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- [here](https://aihub.qualcomm.com/models/conditional_detr_resnet50).
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-
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-
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-
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- ### Model Details
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-
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- - **Model Type:** Model_use_case.object_detection
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- - **Model Stats:**
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- - Model checkpoint: ResNet50
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- - Input resolution: 480x480
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- - Number of parameters: 43.6M
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- - Model size (float): 166 MB
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-
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- | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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- |---|---|---|---|---|---|---|---|---|
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- | Conditional-DETR-ResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 98.411 ms | 0 - 320 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 93.438 ms | 4 - 286 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 44.596 ms | 0 - 368 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 45.93 ms | 5 - 342 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 22.663 ms | 0 - 3 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 20.552 ms | 5 - 7 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 20.633 ms | 0 - 94 MB | NPU | [Conditional-DETR-ResNet50.onnx.zip](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.onnx.zip) |
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- | Conditional-DETR-ResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 145.488 ms | 0 - 317 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 134.302 ms | 0 - 282 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 98.411 ms | 0 - 320 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 93.438 ms | 4 - 286 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 34.645 ms | 0 - 279 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 34.241 ms | 0 - 251 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 145.488 ms | 0 - 317 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 134.302 ms | 0 - 282 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 16.313 ms | 0 - 422 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 14.461 ms | 5 - 389 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 15.219 ms | 5 - 381 MB | NPU | [Conditional-DETR-ResNet50.onnx.zip](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.onnx.zip) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 11.79 ms | 0 - 328 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 11.629 ms | 5 - 303 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 11.779 ms | 1 - 282 MB | NPU | [Conditional-DETR-ResNet50.onnx.zip](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.onnx.zip) |
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- | Conditional-DETR-ResNet50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 9.301 ms | 0 - 399 MB | NPU | [Conditional-DETR-ResNet50.tflite](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.tflite) |
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- | Conditional-DETR-ResNet50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 9.031 ms | 5 - 381 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 8.995 ms | 5 - 277 MB | NPU | [Conditional-DETR-ResNet50.onnx.zip](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.onnx.zip) |
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- | Conditional-DETR-ResNet50 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 21.344 ms | 5 - 5 MB | NPU | [Conditional-DETR-ResNet50.dlc](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.dlc) |
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- | Conditional-DETR-ResNet50 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 21.362 ms | 83 - 83 MB | NPU | [Conditional-DETR-ResNet50.onnx.zip](https://huggingface.co/qualcomm/Conditional-DETR-ResNet50/blob/main/Conditional-DETR-ResNet50.onnx.zip) |
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-
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-
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-
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-
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- ## Installation
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-
70
-
71
- Install the package via pip:
72
- ```bash
73
- # NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
74
- pip install "qai-hub-models[conditional-detr-resnet50]"
75
- ```
76
-
77
-
78
- ## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
79
-
80
- Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) with your
81
- Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
82
-
83
- With this API token, you can configure your client to run models on the cloud
84
- hosted devices.
85
- ```bash
86
- qai-hub configure --api_token API_TOKEN
87
- ```
88
- Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.
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-
90
-
91
-
92
- ## Demo off target
93
-
94
- The package contains a simple end-to-end demo that downloads pre-trained
95
- weights and runs this model on a sample input.
96
-
97
- ```bash
98
- python -m qai_hub_models.models.conditional_detr_resnet50.demo
99
- ```
100
-
101
- The above demo runs a reference implementation of pre-processing, model
102
- inference, and post processing.
103
-
104
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
105
- environment, please add the following to your cell (instead of the above).
106
- ```
107
- %run -m qai_hub_models.models.conditional_detr_resnet50.demo
108
- ```
109
-
110
-
111
- ### Run model on a cloud-hosted device
112
-
113
- In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
114
- device. This script does the following:
115
- * Performance check on-device on a cloud-hosted device
116
- * Downloads compiled assets that can be deployed on-device for Android.
117
- * Accuracy check between PyTorch and on-device outputs.
118
-
119
- ```bash
120
- python -m qai_hub_models.models.conditional_detr_resnet50.export
121
- ```
122
-
123
-
124
-
125
- ## How does this work?
126
-
127
- This [export script](https://aihub.qualcomm.com/models/conditional_detr_resnet50/qai_hub_models/models/Conditional-DETR-ResNet50/export.py)
128
- leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
129
- on-device. Lets go through each step below in detail:
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-
131
- Step 1: **Compile model for on-device deployment**
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-
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- To compile a PyTorch model for on-device deployment, we first trace the model
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- in memory using the `jit.trace` and then call the `submit_compile_job` API.
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-
136
- ```python
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- import torch
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-
139
- import qai_hub as hub
140
- from qai_hub_models.models.conditional_detr_resnet50 import Model
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-
142
- # Load the model
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- torch_model = Model.from_pretrained()
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-
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- # Device
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- device = hub.Device("Samsung Galaxy S25")
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-
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- # Trace model
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- input_shape = torch_model.get_input_spec()
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- sample_inputs = torch_model.sample_inputs()
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-
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- pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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-
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- # Compile model on a specific device
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- compile_job = hub.submit_compile_job(
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- model=pt_model,
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- device=device,
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- input_specs=torch_model.get_input_spec(),
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- )
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-
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- # Get target model to run on-device
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- target_model = compile_job.get_target_model()
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-
164
- ```
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-
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-
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- Step 2: **Performance profiling on cloud-hosted device**
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-
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- After compiling models from step 1. Models can be profiled model on-device using the
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- `target_model`. Note that this scripts runs the model on a device automatically
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- provisioned in the cloud. Once the job is submitted, you can navigate to a
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- provided job URL to view a variety of on-device performance metrics.
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- ```python
174
- profile_job = hub.submit_profile_job(
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- model=target_model,
176
- device=device,
177
- )
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-
179
- ```
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-
181
- Step 3: **Verify on-device accuracy**
182
-
183
- To verify the accuracy of the model on-device, you can run on-device inference
184
- on sample input data on the same cloud hosted device.
185
- ```python
186
- input_data = torch_model.sample_inputs()
187
- inference_job = hub.submit_inference_job(
188
- model=target_model,
189
- device=device,
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- inputs=input_data,
191
- )
192
- on_device_output = inference_job.download_output_data()
193
-
194
- ```
195
- With the output of the model, you can compute like PSNR, relative errors or
196
- spot check the output with expected output.
197
-
198
- **Note**: This on-device profiling and inference requires access to Qualcomm®
199
- AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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-
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-
202
-
203
- ## Run demo on a cloud-hosted device
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-
205
- You can also run the demo on-device.
206
-
207
- ```bash
208
- python -m qai_hub_models.models.conditional_detr_resnet50.demo --eval-mode on-device
209
- ```
210
-
211
- **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
212
- environment, please add the following to your cell (instead of the above).
213
- ```
214
- %run -m qai_hub_models.models.conditional_detr_resnet50.demo -- --eval-mode on-device
215
- ```
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-
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-
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- ## Deploying compiled model to Android
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-
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-
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- The models can be deployed using multiple runtimes:
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- - TensorFlow Lite (`.tflite` export): [This
223
- tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
224
- guide to deploy the .tflite model in an Android application.
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-
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-
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- - QNN (`.so` export ): This [sample
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- app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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- provides instructions on how to use the `.so` shared library in an Android application.
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-
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-
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- ## View on Qualcomm® AI Hub
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- Get more details on Conditional-DETR-ResNet50's performance across various devices [here](https://aihub.qualcomm.com/models/conditional_detr_resnet50).
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- Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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-
236
 
237
  ## License
238
  * The license for the original implementation of Conditional-DETR-ResNet50 can be found
239
  [here](https://github.com/huggingface/transformers/blob/main/LICENSE).
240
 
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-
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-
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  ## References
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  * [Conditional {DETR} for Fast Training Convergence](https://arxiv.org/abs/2108.06152)
245
  * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr)
246
 
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-
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-
249
  ## Community
250
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
251
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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-
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-
 
9
 
10
  ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/web-assets/model_demo.png)
11
 
12
+ # Conditional-DETR-ResNet50: Optimized for Qualcomm Devices
 
 
13
 
14
  DETR is a machine learning model that can detect objects (trained on COCO dataset).
15
 
16
+ This is based on the implementation of Conditional-DETR-ResNet50 found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr).
17
+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/conditional_detr_resnet50) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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+
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+ 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.
20
+
21
+ ## Getting Started
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+ There are two ways to deploy this model on your device:
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+
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+ ### Option 1: Download Pre-Exported Models
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+
26
+ Below are pre-exported model assets ready for deployment.
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+
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+ | Runtime | Precision | Chipset | SDK Versions | Download |
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+ |---|---|---|---|---|
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+ | ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/releases/v0.46.1/conditional_detr_resnet50-onnx-float.zip)
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+ | QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/releases/v0.46.1/conditional_detr_resnet50-qnn_dlc-float.zip)
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+ | TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/releases/v0.46.1/conditional_detr_resnet50-tflite-float.zip)
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+
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+ For more device-specific assets and performance metrics, visit **[Conditional-DETR-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/conditional_detr_resnet50)**.
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+
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+
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+ ### Option 2: Export with Custom Configurations
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+
39
+ Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/conditional_detr_resnet50) Python library to compile and export the model with your own:
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+ - Custom weights (e.g., fine-tuned checkpoints)
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+ - Custom input shapes
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+ - Target device and runtime configurations
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+
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+ This option is ideal if you need to customize the model beyond the default configuration provided here.
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+
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+ See our repository for [Conditional-DETR-ResNet50 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/conditional_detr_resnet50) for usage instructions.
47
+
48
+ ## Model Details
49
+
50
+ **Model Type:** Model_use_case.object_detection
51
+
52
+ **Model Stats:**
53
+ - Model checkpoint: ResNet50
54
+ - Input resolution: 480x480
55
+ - Number of parameters: 43.6M
56
+ - Model size (float): 166 MB
57
+
58
+ ## Performance Summary
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+ | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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+ |---|---|---|---|---|---|---
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+ | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® X Elite | 21.551 ms | 83 - 83 MB | NPU
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+ | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 15.521 ms | 0 - 379 MB | NPU
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+ | Conditional-DETR-ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 21.561 ms | 0 - 94 MB | NPU
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+ | Conditional-DETR-ResNet50 | ONNX | float | Qualcomm® QCS9075 | 33.407 ms | 4 - 12 MB | NPU
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+ | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.775 ms | 0 - 283 MB | NPU
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+ | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.926 ms | 0 - 276 MB | NPU
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+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 23.12 ms | 5 - 5 MB | NPU
68
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 16.723 ms | 3 - 436 MB | NPU
69
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 98.38 ms | 0 - 326 MB | NPU
70
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 22.964 ms | 5 - 7 MB | NPU
71
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 145.739 ms | 1 - 327 MB | NPU
72
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 37.338 ms | 7 - 13 MB | NPU
73
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 46.462 ms | 4 - 372 MB | NPU
74
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 98.38 ms | 0 - 326 MB | NPU
75
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 34.708 ms | 0 - 279 MB | NPU
76
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.53 ms | 5 - 342 MB | NPU
77
+ | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.878 ms | 5 - 348 MB | NPU
78
+ | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 16.973 ms | 24 - 495 MB | NPU
79
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 92.649 ms | 0 - 367 MB | NPU
80
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 22.1 ms | 0 - 3 MB | NPU
81
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 29.851 ms | 0 - 426 MB | NPU
82
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 33.136 ms | 0 - 93 MB | NPU
83
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 45.579 ms | 0 - 407 MB | NPU
84
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 92.649 ms | 0 - 367 MB | NPU
85
+ | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 34.871 ms | 0 - 316 MB | NPU
86
+ | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.897 ms | 0 - 361 MB | NPU
87
+ | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.205 ms | 0 - 462 MB | NPU
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88
 
89
  ## License
90
  * The license for the original implementation of Conditional-DETR-ResNet50 can be found
91
  [here](https://github.com/huggingface/transformers/blob/main/LICENSE).
92
 
 
 
93
  ## References
94
  * [Conditional {DETR} for Fast Training Convergence](https://arxiv.org/abs/2108.06152)
95
  * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_detr)
96
 
 
 
97
  ## Community
98
  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
99
  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
 
tool-versions.yaml DELETED
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- tool_versions:
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- onnx:
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- qairt: 2.37.1.250807093845_124904
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- onnx_runtime: 1.23.0