DETR ResNet-50
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve facebook/detr-resnet-50New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
DETR ResNet-50
DETR (DEtection TRansformer) by Meta is a pioneering end-to-end object detection model that treats detection as a direct set prediction problem, eliminating the need for hand-crafted components like anchor boxes and NMS.
Usage
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
inputs = processor(images=Image.open("image.jpg"), return_tensors="pt")
outputs = model(**inputs)
# Convert to COCO format
results = processor.post_process_object_detection(outputs, threshold=0.9)When to use
- Research - transformer-based detection baseline
- Simple deployment - no anchor tuning needed
- Panoptic segmentation - DETR variants support it
Frequently asked
Quick answers to common questions
How much VRAM does DETR ResNet-50 need?
DETR ResNet-50 with 0.04B parameters needs significant VRAM depending on quantization. Use our VRAM calculator for an exact estimate.
Is DETR ResNet-50 better than other DETR models?
DETR ResNet-50 has 0.04B parameters with 8,192 context - a strong choice for object-detection.
What license is DETR ResNet-50 under?
DETR ResNet-50 is released under the Apache 2.0 license, making it suitable for most commercial and personal projects.
What hardware runs DETR ResNet-50 well?
With 0.04B parameters, DETR ResNet-50 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for DETR ResNet-50?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with DETR ResNet-50?
DETR ResNet-50 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
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