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{}
---
# object-detection: detr-finetuned-thermal-dogs-and-people
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of [DETR](https://huggingface.co/facebook/detr-resnet-50) on the Roboflow [Thermal Dogs and People](https://public.roboflow.com/object-detection/thermal-dogs-and-people/1) dataset.
It achieves the following results on the evaluation set:
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.870
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.778
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.489
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.720
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.641
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.733
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.746
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.794
```
## Intended purpose
Main purpose for this model are solely for learning purposes.
Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.
## Training and evaluation data
Data can be seen at [Weights and Biases](https://wandb.ai/faldeus0092/thermal-dogs-and-people/runs/zjt8bp9x?workspace=user-faldeus0092)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-4
- lr_backbone: 1e-5
- weight_decay: 1e-4
- optimizer: AdamW
- train_batch_size: 4
- eval_batch_size: 2
- train_set: 142
- test_set: 41
- num_epochs: 68
### Example usage (transformers pipeline)
```py
# Use a pipeline as a high-level helper
from transformers import pipeline
image = Image.open('/content/Thermal-Dogs-and-People-1/test/IMG_0006 5_jpg.rf.cd46e6a862d6ffb7fce6795067ce7cc7.jpg')
# image = Image.open(requests.get(url, stream=True).raw) # if you want to open from url
obj_detector = pipeline("object-detection", model="faldeus0092/detr-finetuned-thermal-dogs-and-people")
draw = ImageDraw.Draw(image)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
x, y, x2, y2 = tuple(box)
draw.rectangle((x, y, x2, y2), outline="red", width=1)
draw.text((x, y), model.config.id2label[label.item()], fill="white")
image
```
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