Florence-2

Microsoft's powerful vision model for captioning, detection, segmentation, and more.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

All examples in this guide can be run on GPU servers rented through CLORE.AI Marketplace marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is Florence-2?

Florence-2 by Microsoft is a vision foundation model that handles:

• Image captioning (brief and detailed)

• Object detection and localization

• Dense region captioning

• Referring expression comprehension

• OCR and text recognition

• Visual question answering

Resources

• HuggingFace: microsoft/Florence-2-large

• Paper: Florence-2 Paper

• GitHub: microsoft/Florence-2

• Demo: HuggingFace Space

Recommended Hardware

Component	Minimum	Recommended	Optimal
GPU	RTX 3060 12GB	RTX 4080 16GB	RTX 4090 24GB
VRAM	8GB	12GB	16GB
CPU	4 cores	8 cores	16 cores
RAM	16GB	32GB	64GB
Storage	30GB SSD	50GB NVMe	100GB NVMe
Internet	100 Mbps	500 Mbps	1 Gbps

Quick Deploy on CLORE.AI

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

pip install transformers accelerate einops timm gradio && \
python -c "
import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import torch
from PIL import Image

model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', torch_dtype=torch.float16, trust_remote_code=True).to('cuda')
processor = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True)

def process(image, task):
    inputs = processor(text=task, images=image, return_tensors='pt').to('cuda', torch.float16)
    generated_ids = model.generate(input_ids=inputs['input_ids'], pixel_values=inputs['pixel_values'], max_new_tokens=1024)
    result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    return processor.post_process_generation(result, task=task, image_size=image.size)

gr.Interface(fn=process, inputs=[gr.Image(type='pil'), gr.Dropdown(['<CAPTION>', '<DETAILED_CAPTION>', '<OD>'])], outputs='json').launch(server_name='0.0.0.0')
"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install transformers accelerate einops timm
pip install flash-attn --no-build-isolation  # Optional, for faster inference

What You Can Create

Content Analysis

• Auto-generate image descriptions

• Extract text from images (OCR)

• Analyze visual content at scale

Data Annotation

• Auto-label datasets with captions

• Generate bounding boxes for objects

• Create dense annotations

Accessibility

• Generate alt-text for images

• Describe images for visually impaired

• Create audio descriptions

Search & Discovery

• Index images by content

• Build visual search systems

• Content moderation

Document Processing

• Extract text from documents

• Understand charts and diagrams

• Process scanned materials

Basic Usage

Image Captioning

from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import torch

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-large",
    torch_dtype=torch.float16,
    trust_remote_code=True
).to("cuda")

processor = AutoProcessor.from_pretrained(
    "microsoft/Florence-2-large",
    trust_remote_code=True
)

image = Image.open("photo.jpg")

# Brief caption
task = "<CAPTION>"
inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
caption = processor.post_process_generation(result, task=task, image_size=image.size)
print(caption)

# Output: {'<CAPTION>': 'A dog playing in the park'}

# Detailed caption
task = "<DETAILED_CAPTION>"
inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
detailed = processor.post_process_generation(result, task=task, image_size=image.size)
print(detailed)

Object Detection

task = "<OD>"  # Object Detection
inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
detections = processor.post_process_generation(result, task=task, image_size=image.size)

# Output: {'<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['dog', 'ball', ...]}}

OCR (Text Recognition)

task = "<OCR>"
inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
text = processor.post_process_generation(result, task=task, image_size=image.size)
print(text)

# Output: {'<OCR>': 'Text found in the image...'}

Dense Region Captioning

task = "<DENSE_REGION_CAPTION>"
inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
regions = processor.post_process_generation(result, task=task, image_size=image.size)

# Output: {'<DENSE_REGION_CAPTION>': {'bboxes': [...], 'labels': ['a brown dog running', 'green grass', ...]}}

Referring Expression Comprehension

Find objects based on text descriptions:

task = "<CAPTION_TO_PHRASE_GROUNDING>"
text_input = "the red car on the left"

inputs = processor(
    text=task + text_input,
    images=image,
    return_tensors="pt"
).to("cuda", torch.float16)

generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024
)
result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
grounding = processor.post_process_generation(result, task=task, image_size=image.size)

# Returns bounding box of "the red car on the left"

All Available Tasks

TASKS = [
    "<CAPTION>",                    # Brief caption
    "<DETAILED_CAPTION>",           # Detailed description
    "<MORE_DETAILED_CAPTION>",      # Very detailed description
    "<OD>",                          # Object detection
    "<DENSE_REGION_CAPTION>",       # Region descriptions
    "<REGION_PROPOSAL>",            # Propose regions of interest
    "<CAPTION_TO_PHRASE_GROUNDING>", # Find objects from text
    "<REFERRING_EXPRESSION_SEGMENTATION>", # Segment from text
    "<REGION_TO_SEGMENTATION>",     # Segment specified region
    "<OPEN_VOCABULARY_DETECTION>",  # Detect with text labels
    "<REGION_TO_CATEGORY>",         # Classify region
    "<REGION_TO_DESCRIPTION>",      # Describe region
    "<OCR>",                         # Extract text
    "<OCR_WITH_REGION>",            # Extract text with locations
]

Batch Processing

import os
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import torch
import json

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-large",
    torch_dtype=torch.float16,
    trust_remote_code=True
).to("cuda")
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)

def process_image(image_path, task):
    image = Image.open(image_path)
    inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024
    )
    result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    return processor.post_process_generation(result, task=task, image_size=image.size)

# Process directory
input_dir = "./images"
results = {}

for filename in os.listdir(input_dir):
    if not filename.endswith(('.jpg', '.png')):
        continue

    path = os.path.join(input_dir, filename)
    results[filename] = {
        "caption": process_image(path, "<CAPTION>"),
        "objects": process_image(path, "<OD>"),
        "text": process_image(path, "<OCR>")
    }
    print(f"Processed: {filename}")

with open("results.json", "w") as f:
    json.dump(results, f, indent=2)

Gradio Interface

import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image, ImageDraw
import torch

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-large",
    torch_dtype=torch.float16,
    trust_remote_code=True
).to("cuda")
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)

def run_task(image, task):
    inputs = processor(text=task, images=image, return_tensors="pt").to("cuda", torch.float16)
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024
    )
    result = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed = processor.post_process_generation(result, task=task, image_size=image.size)

    # Draw boxes if detection task
    output_image = image.copy()
    if task in ["<OD>", "<DENSE_REGION_CAPTION>"]:
        draw = ImageDraw.Draw(output_image)
        if "bboxes" in parsed.get(task, {}):
            for box, label in zip(parsed[task]["bboxes"], parsed[task]["labels"]):
                draw.rectangle(box, outline="red", width=2)
                draw.text((box[0], box[1]-15), label, fill="red")

    return output_image, str(parsed)

demo = gr.Interface(
    fn=run_task,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Dropdown(
            choices=["<CAPTION>", "<DETAILED_CAPTION>", "<OD>", "<DENSE_REGION_CAPTION>", "<OCR>"],
            value="<CAPTION>",
            label="Task"
        )
    ],
    outputs=[
        gr.Image(label="Result"),
        gr.Textbox(label="Output", lines=10)
    ],
    title="Florence-2 Vision AI",
    description="Multi-task vision model. Running on CLORE.AI GPU servers."
)

demo.launch(server_name="0.0.0.0", server_port=7860)

Performance

Task	Resolution	GPU	Speed
Caption	768x768	RTX 3090	200ms
Caption	768x768	RTX 4090	120ms
Object Detection	768x768	RTX 4090	150ms
OCR	768x768	RTX 4090	180ms
Dense Caption	768x768	A100	100ms

Model Variants

Model	Parameters	VRAM	Speed
Florence-2-base	232M	4GB	Fast
Florence-2-large	771M	8GB	Medium
Florence-2-base-ft	232M	4GB	Fast
Florence-2-large-ft	771M	8GB	Medium

Common Problems & Solutions

Out of Memory

Problem: CUDA OOM error

Solutions:

# Use base model instead of large
model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-base",
    torch_dtype=torch.float16,
    trust_remote_code=True
).to("cuda")

# Or enable CPU offload
model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Florence-2-large",
    torch_dtype=torch.float16,
    trust_remote_code=True,
    device_map="auto"
)

Slow Inference

Problem: Processing takes too long

Solutions:

• Use Florence-2-base for faster inference

• Install flash-attention for speedup

• Batch multiple images together

• Use A100 GPU for production

pip install flash-attn --no-build-isolation

Poor OCR Results

Problem: Text recognition is inaccurate

Solutions:

• Ensure image is high resolution (at least 768px)

• Use <OCR_WITH_REGION> for better localization

• Pre-process: enhance contrast, deskew image

• Crop to text regions before OCR

Detection Missing Objects

Problem: Objects not detected

Solutions:

• Use <DENSE_REGION_CAPTION> for more regions

• Try <OPEN_VOCABULARY_DETECTION> with specific labels

• Combine with GroundingDINO for specific objects

Troubleshooting

Task not working

• Check exact task name syntax

• Some tasks need specific input format

• Verify model version matches task

Output format unexpected

• Different tasks return different formats

• Parse output according to task type

• Check documentation for task outputs

CUDA memory issues

• Florence-2-large needs ~8GB VRAM

• Use Florence-2-base for less memory

• Enable gradient checkpointing

Slow processing

• Use batch inference when possible

• Enable FP16 mode

• Consider TensorRT optimization

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• LLaVA - Vision chat and QA

• GroundingDINO - Zero-shot detection

• SAM2 - Segment detected objects