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--- |
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license: agpl-3.0 |
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library: ultralytics |
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tags: |
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- object-detection |
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- pytorch |
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- ultralytics |
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- roboflow-universe |
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- human-detection |
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- yolov8 |
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--- |
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# Human Detection using Thermal Camera |
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## Use Case |
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This model is can be used for detecting humans from thermal images. This should work on both Pseudo-color and Grayscale thermal images. The model was fine tuned for humans only but can be finetuned further fort detecting other objects using Thermal images. |
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To deploy this model use the following code: |
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- Install dependencies: |
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```bash |
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$ python -m pip install ultralytics supervision huggingface_hub |
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``` |
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- Python code |
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```python |
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# import libraries |
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from huggingface_hub import hf_hub_download |
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from ultralytics import YOLO |
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from supervision import Detections |
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import cv |
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# download model |
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model_path = hf_hub_download( |
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repo_id = "pitangent-ds/YOLOv8-human-detection-thermal", |
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filename = "model.pt" |
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) |
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# load model |
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model = YOLO(model_path) |
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# method for inference |
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def inference(image_path): |
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cv_image = cv.imread(image_path, cv2.IMREAD_ANYCOLOR) |
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model_output = model(cv_image, conf=0.6, verbose=False) |
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detections = Detections.from_ultralytics(model_output[0]) |
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return detections |
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``` |
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## Training Code |
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- Dataset Link: [Roboflow Universe](https://universe.roboflow.com/smart2/persondection-61bc2) |
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```python |
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from ultralytics import YOLO |
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import torch |
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# load model |
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model = YOLO("yolov8n.pt") |
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# hyper parameters |
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hyperparams = { |
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"batch": 32, |
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"epochs": 30, |
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"imgsz": [640, 480], |
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"optimizer": "AdamW", |
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"cos_lr": True, |
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"lr0": 3e-5, |
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"warmup_epochs": 10 |
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} |
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# start training |
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model.train( |
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device = 'cuda' if torch.cuda.is_available() else 'cpu', |
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data = "data.yaml", |
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**hyperparams |
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) |
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``` |
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- Click here for: [Training Arguments](./training_artifacts/args.yaml) |
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## Libraries |
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```yaml |
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python: 3.10.13 |
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ultralytics: 8.0.206 |
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torch: "2.1.0+cu118" |
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roboflow: 1.1.9 |
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``` |