Florence 2 Large Ft Moredetailed

--- license: mit base_model: - microsoft/Florence-2-large-ft datasets: - google/imageinwords language: - en library_name: transformers pipeline_tag: image-to-text tags: - image-captioning --- # Florence-2-large-ft trained on task MORE_DETAILED_CAPTION with imageinwords ## Usage ### Single Image ```python from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM name = "yayayaaa/florence-2-large-ft-moredetailed" model = AutoModelForCausalLM.from_pretrained(name, trust_remote_code=True).to("cuda").eval() processor = AutoProcessor.from_pretrained(name, trust_remote_code=True) prompt = "<MORE_DETAILED_CAPTION>" filename = 'test.jpg' image = Image.open(filename).convert('RGB') inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda") generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, do_sample=False, ) print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0]) ``` ### Batch Install accelerate for device_map Can do batch_size=28 for 24GB VRAM ```python import sys import torch from tqdm import tqdm from pathlib import Path from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM from torch.utils.data import Dataset, DataLoader task = "<MORE_DETAILED_CAPTION>" name="yayayaaa/florence-2-large-ft-moredetailed" model = AutoModelForCausalLM.from_pretrained(name, trust_remote_code=True, device_map="auto") processor = AutoProcessor.from_pretrained(name, trust_remote_code=True, device_map="auto") #model.language_model.generate = torch.compile(model.language_model.generate) # BATCH SIZE batch_size=28 directory = Path(sys.argv[1]) patterns = ['**/*.jpg', '**/*.jpeg', '**/*.png'] patterns += [p.upper() for p in patterns] filenames = [str(fn) for pattern in patterns for fn in directory.glob(pattern)] class Data(Dataset): def __init__(self, data): self.name="Data" self.data=data def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] data = DataLoader(Data(filenames), batch_size=batch_size, num_workers=0, shuffle=False) @torch.inference_mode() def process_images(images): inputs = processor(text=[task]*len(images), images=images, return_tensors="pt").to("cuda") generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_v...

Source context: 39 downloads · 16 likes · Pipeline image-to-text · Library transformers · Repo yayayaaa/florence-2-large-ft-moredetailed