Instructions to use Sandesh-Lav/potato-caption-blip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandesh-Lav/potato-caption-blip with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sandesh-Lav/potato-caption-blip")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Sandesh-Lav/potato-caption-blip")
model = AutoModelForImageTextToText.from_pretrained("Sandesh-Lav/potato-caption-blip")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sandesh-Lav/potato-caption-blip with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandesh-Lav/potato-caption-blip"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandesh-Lav/potato-caption-blip",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sandesh-Lav/potato-caption-blip

SGLang

How to use Sandesh-Lav/potato-caption-blip 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 "Sandesh-Lav/potato-caption-blip" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandesh-Lav/potato-caption-blip",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Sandesh-Lav/potato-caption-blip" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandesh-Lav/potato-caption-blip",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sandesh-Lav/potato-caption-blip with Docker Model Runner:
```
docker model run hf.co/Sandesh-Lav/potato-caption-blip
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Potato Tuber Disease Captioning Model (BLIP)

This model is a fine-tuned version of Salesforce/blip-image-captioning-base trained to generate descriptive captions for potato tuber diseases.

The model analyzes an input image of a potato tuber and produces a natural language description of the visible disease symptoms.

Example output:

A cut-section of a potato tuber showing moderate dry rot within the internal tissue.

Dataset

Training was performed using the Sandesh-Lav/potato-tuber-caption-dataset.

Dataset characteristics:

Property	Value
Total images	~1846
Train split	1661
Validation split	92
Test split	93

Each image is paired with a scientific description of potato tuber diseases, including:

bacterial soft rot
dry rot
greening disorder
internal tissue damage

Training

The model was fine-tuned using the Hugging Face Transformers Trainer.

Training configuration:

Parameter	Value
Base model	BLIP image captioning
Epochs	5
Batch size	4
Learning rate	5e-5
Optimizer	AdamW

Training loss after fine-tuning:

Training loss ≈ 0.19

Evaluation

Evaluation was performed on the held-out test set.

Metrics used:

Metric	Score
BLEU-4	0.74
ROUGE-L	0.95
METEOR	0.92
CIDEr	5.43

The high scores indicate that the generated captions closely match the reference disease descriptions.

Note: The dataset contains structured scientific captions, which may lead to higher similarity scores compared to open-domain caption datasets.

Usage

Load the model using Hugging Face Transformers:

from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image

processor = BlipProcessor.from_pretrained("Sandesh-Lav/potato-caption-blip")
model = BlipForConditionalGeneration.from_pretrained("Sandesh-Lav/potato-caption-blip")

image = Image.open("potato.jpg").convert("RGB")

inputs = processor(images=image, return_tensors="pt")

out = model.generate(**inputs, max_new_tokens=40)

caption = processor.decode(out[0], skip_special_tokens=True)

print(caption)

Example output:

A potato tuber showing severe bacterial soft rot with tissue degradation.

Applications

This model can be used for:

agricultural disease documentation
automated crop disease reporting
smart farming systems
agricultural AI research

Limitations

The dataset contains structured captions describing potato tuber diseases.
The model may struggle with images outside this domain.
Performance may decrease on different lighting conditions or potato varieties.

Citation

If you use this model, please cite the dataset and BLIP architecture.

Author

Developed by as part of an academic mini-project.

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Safetensors

Model size

0.2B params

Tensor type

F32

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Sandesh-Lav
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potato-caption-blip