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issai
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TFW

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Object Detection
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Tags:
thermal faces
thermal facial landmarks
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  # TFW: Annotated Thermal Faces in the Wild Dataset Card
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- **Repository:** [https://github.com/IS2AI/TFW](https://github.com/IS2AI/TFW)
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-
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  **Summary:**
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- The TFW dataset provides a collection of thermal face images captured in diverse environments: controlled indoor (c-indoor), semi-controlled indoor (s-indoor), and uncontrolled outdoor (u-outdoor). It's annotated with bounding boxes and 5-point facial landmarks. The dataset includes images from the SpeakingFaces dataset ([https://github.com/IS2AI/SpeakingFaces](https://github.com/IS2AI/SpeakingFaces)) for the c-indoor subset, and additional data collected using a FLIR T540 thermal camera for s-indoor and u-outdoor subsets. The paper detailing the dataset and accompanying pre-trained YOLOv5 and YOLOv5Face models are also available.
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- **Dataset Statistics:**
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- | Environment | Subjects | Images | Labeled Faces | Visual Pair |
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- |--------------|---------|--------|-----------------|-------------|
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  | c-indoor | 142 | 5,112 | 5,112 | yes |
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  | s-indoor | 9 | 780 | 1,748 | yes |
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  | u-outdoor | 15 | 4,090 | 9,649 | no |
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- | **combined** | **147** | **9,982** | **16,509** | yes & no |
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  **Example Images:**
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- [Image would be displayed here. Source: https://github.com/IS2AI/TFW/blob/main/figures/example.png]
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- **Pre-trained Models:**
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- The following table summarizes the performance of pre-trained YOLOv5 and YOLOv5Face models on the TFW dataset. Note that links to the models are not included here as per the instructions.
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- | Model | Backbone | c-indoor AP<sub>50</sub> | u-outdoor AP<sub>50</sub> | Speed (ms) V100 b1 | Params (M) | Flops (G) @512x384 |
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- |-----------------|--------------|------------------------|------------------------|----------------------|-------------|--------------------|
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- | YOLOv5n | CSPNet | 100 | 97.29 | 6.16 | 1.76 | 0.99 |
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- | YOLOv5n6 | CSPNet | 100 | 95.79 | 8.18 | 3.09 | 1.02 |
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- | YOLOv5s | CSPNet | 100 | 96.82 | 7.20 | 7.05 | 3.91 |
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- | YOLOv5s6 | CSPNet | 100 | 96.83 | 9.05 | 12.31 | 3.88 |
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- | YOLOv5m | CSPNet | 100 | 97.16 | 9.59 | 21.04 | 12.07 |
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- | YOLOv5m6 | CSPNet | 100 | 97.10 | 12.11 | 35.25 | 11.76 |
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- | YOLOv5l | CSPNet | 100 | 96.68 | 12.39 | 46.60 | 27.38 |
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- | YOLOv5l6 | CSPNet | 100 | 96.29 | 15.73 | 76.16 | 110.2 |
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- | YOLOv5n-Face | ShuffleNetv2 | 100 | 95.93 | 10.12 | 1.72 | 1.36 |
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- | YOLOv5n6-Face | ShuffleNetv2 | 100 | 95.59 | 13.30 | 2.54 | 1.38 |
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- | YOLOv5s-Face | CSPNet | 100 | 96.73 | 8.29 | 7.06 | 3.67 |
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- | YOLOv5s6-Face | CSPNet | 100 | 96.36 | 10.86 | 12.37 | 3.75 |
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- | YOLOv5m-Face | CSPNet | 100 | 95.32 | 11.01 | 21.04 | 11.58 |
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- | YOLOv5m6-Face | CSPNet | 100 | 96.32 | 13.97 | 35.45 | 11.84 |
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- | YOLOv5l-Face | CSPNet | 100 | 96.18 | 13.57 | 46.59 | 25.59 |
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- | YOLOv5l6-Face | CSPNet | 100 | 95.76 | 17.29 | 76.67 | 113.2 |
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  **Citation:**
@@ -58,4 +55,12 @@ The following table summarizes the performance of pre-trained YOLOv5 and YOLOv5F
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  doi={10.1109/TIFS.2022.3177949}}
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  ```
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- **(Note: Example images and demonstration GIF would be included in the actual Hugging Face dataset card. Links to pre-trained models have been omitted as requested.)**
 
 
 
 
 
 
 
 
 
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  # TFW: Annotated Thermal Faces in the Wild Dataset Card
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  **Summary:**
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+ The TFW dataset is a collection of thermal face images captured in controlled indoor, semi-controlled indoor, and uncontrolled outdoor environments. It features manual annotations of face bounding boxes and five facial landmarks. This dataset aims to advance research in thermal face detection and recognition. The repository also provides pre-trained YOLOv5 and YOLO5Face models trained on this dataset, making it a valuable resource for researchers working in this field.
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+ **Dataset Summary Table:**
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+ | Environment | Subjects | Images | Labeled faces | Visual pair |
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+ |--------------|----------|--------|----------------|-------------|
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  | c-indoor | 142 | 5,112 | 5,112 | yes |
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  | s-indoor | 9 | 780 | 1,748 | yes |
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  | u-outdoor | 15 | 4,090 | 9,649 | no |
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+ | combined | 147 | 9,982 | 16,509 | yes & no |
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  **Example Images:**
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+ [Image](https://github.com/IS2AI/TFW/blob/main/figures/example.png)
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+ **Pre-trained Models Performance:**
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+ | Model | Backbone | c-indoor AP<sub>50</sub> | u-outdoor AP<sub>50</sub> | Speed (ms) V100 b1 | Params (M) | Flops (G) @512x384 |
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+ |-----------------|---------------|------------------------|-------------------------|---------------------|------------|--------------------|
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+ | YOLOv5n | CSPNet | 100 | 97.29 | 6.16 | 1.76 | 0.99 |
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+ | YOLOv5n6 | CSPNet | 100 | 95.79 | 8.18 | 3.09 | 1.02 |
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+ | YOLOv5s | CSPNet | 100 | 96.82 | 7.20 | 7.05 | 3.91 |
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+ | YOLOv5s6 | CSPNet | 100 | 96.83 | 9.05 | 12.31 | 3.88 |
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+ | YOLOv5m | CSPNet | 100 | 97.16 | 9.59 | 21.04 | 12.07 |
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+ | YOLOv5m6 | CSPNet | 100 | 97.10 | 12.11 | 35.25 | 11.76 |
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+ | YOLOv5l | CSPNet | 100 | 96.68 | 12.39 | 46.60 | 27.38 |
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+ | YOLOv5l6 | CSPNet | 100 | 96.29 | 15.73 | 76.16 | 110.2 |
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+ | YOLOv5n-Face | ShuffleNetv2 | 100 | 95.93 | 10.12 | 1.72 | 1.36 |
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+ | YOLOv5n6-Face | ShuffleNetv2 | 100 | 95.59 | 13.30 | 2.54 | 1.38 |
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+ | YOLOv5s-Face | CSPNet | 100 | 96.73 | 8.29 | 7.06 | 3.67 |
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+ | YOLOv5s6-Face | CSPNet | 100 | 96.36 | 10.86 | 12.37 | 3.75 |
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+ | YOLOv5m-Face | CSPNet | 100 | 95.32 | 11.01 | 21.04 | 11.58 |
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+ | YOLOv5m6-Face | CSPNet | 100 | 96.32 | 13.97 | 35.45 | 11.84 |
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+ | YOLOv5l-Face | CSPNet | 100 | 96.18 | 13.57 | 46.59 | 25.59 |
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+ | YOLOv5l6-Face | CSPNet | 100 | 95.76 | 17.29 | 76.67 | 113.2 |
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  **Citation:**
 
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  doi={10.1109/TIFS.2022.3177949}}
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  ```
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+ **GitHub Repository:** [https://github.com/IS2AI/TFW](https://github.com/IS2AI/TFW)
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+ **Additional Resources:**
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+ * Paper (TechRxiv): [https://www.techrxiv.org/articles/preprint/TFW_Annotated_Thermal_Faces_in_the_Wild_Dataset/17004538](https://www.techrxiv.org/articles/preprint/TFW_Annotated_Thermal_Faces_in_the_Wild_Dataset/17004538) *(Note: Abstract access may be restricted)*
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+ * Paper (IEEE Xplore): [https://ieeexplore.ieee.org/abstract/document/9781417](https://ieeexplore.ieee.org/abstract/document/9781417)
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+ * YouTube Video: [https://www.youtube.com/watch?v=QgXi3rLv1jM](https://www.youtube.com/watch?v=QgXi3rLv1jM)
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+ **(Note: Links to the dataset download locations have been omitted as requested.)**