Instructions to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect", trust_remote_code=True, device_map="auto")

PEFT
How to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect

SGLang

How to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect with Docker Model Runner:
```
docker model run hf.co/davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect
```

LocateAnything-3B clean original-aspect delivery detector R4

Personal, non-commercial LocateAnything one-epoch SFT experiment.

Starting model: nvidia/LocateAnything-3B

Classes: Amazon, UPS, USPS-Truck, Other-Vehicles, FedEx

R4 starts directly from NVIDIA LocateAnything-3B and trains for one epoch on aspect-preserved, patch-aligned images. Every image uses the canonical full five-class prompt, matching R1's detection task while changing only source-image geometry.

Image content is capped at 640px on the longest side without enlargement or aspect distortion, then centered on a neutral-gray canvas aligned to 28px. Training coordinates are remapped to that padded canvas and emitted as normalized integers from 0 to 1000.

Prompt seed: 20260828

Query mixture: {"full": 1.0, "negative": 0.0, "positive_subset": 0.0, "single_positive": 0.0}

License

This model is a derivative of NVIDIA LocateAnything-3B and remains restricted to non-commercial research/evaluation under NVIDIA's model license.

Downloads last month: 33

Safetensors

Model size

4B params

Tensor type

F32

BF16

Model tree for davidr99/locateanything-3b-delivery-detector-r4-clean-original-aspect

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

nvidia/LocateAnything-3B

Finetuned

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this model