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Version: 1.0.0
Category: Object Detection
Algorithm: Swift-YOLO
Dataset: face detection
Class: face
The model is a Swift-YOLO model trained on the face detection dataset.
| Type | Batch | Shape | Remark | |
|---|---|---|---|---|
| Input | image | 1 | [96, 96, 3] | The input image should be resized to 96x96 pixels. |
| Output | bbox | 1 | [567, 6] | The output is a 567x6 tensor, where 567 is the number of candidate boxes and 6 is [x, y, w, h, score, [class]] |
| Backend | Precision | mAP(%) | Flops(M) | Params(M) | Inference(ms) | Download | Author |
|---|---|---|---|---|---|---|---|
| PyTorch | FLOAT32 | 98.70 | 22.641 | 0.699 | - | Link | Seeed Studio |
| ONNX | FLOAT32 | 97.90 | - | 0.699 | - | Link | Seeed Studio |
| TFLite | FLOAT32 | 97.90 | - | - | - | Link | Seeed Studio |
| TFLite | INT8 | 97.90 | - | - | 180.0(1) | Link | Seeed Studio |
| TFLite(vela) | INT8 | 97.90 | - | - | 33(2) | Link | Seeed Studio |
Table Notes:
- Evaluation Parameters: Confidence Threshold: 0.001, IoU Threshold: 0.55, mAP Eval IoU: 0.50..
- Backend: The deep learning framework used to infer the model.
- Precision: The numerical precision used for training the model.
- Metrics: The metrics used to evaluate the model.
- Inference(ms): The inference time of the model in milliseconds.
- 1: xiao_esp32s3.
- 2: grove_vision_ai_we2.
- Link: The link to the model.
- Author: The author of the model.
MIT