Florence-2-large-FormClassification-ft

This model is a fine-tuned version of microsoft/Florence-2-large-ft on an Musa07/Florence_ft dataset. It achieves the following results on the evaluation set:

• Loss: 0.2107

Inference Code

  # Code
    from transformers import AutoProcessor, AutoModelForCausalLM
    import matplotlib.pyplot as plt
    import matplotlib.patches as patches
  
    model = AutoModelForCausalLM.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True, device_map='cuda') # Load the model on GPU if available
    processor = AutoProcessor.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True)
  
    def run_example(task_prompt, image, max_new_tokens=128):
      prompt = task_prompt
      inputs = processor(text=prompt, images=image, return_tensors="pt")
      generated_ids = model.generate(
        input_ids=inputs["input_ids"].cuda(),
        pixel_values=inputs["pixel_values"].cuda(),
        max_new_tokens=max_new_tokens,
        early_stopping=False,
        do_sample=False,
        num_beams=3,
      )
      generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
      parsed_answer = processor.post_process_generation(
          generated_text,
          task=task_prompt,
          image_size=(image.width, image.height)
      )
      return parsed_answer
      
    def plot_bbox(image, data):
      fig, ax = plt.subplots()
  
      # Display the image
      ax.imshow(image)
  
      # Plot each bounding box
      for bbox, label in zip(data['bboxes'], data['labels']):
          # Unpack the bounding box coordinates
          x1, y1, x2, y2 = bbox
          # Create a Rectangle patch
          rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
          # Add the rectangle to the Axes
          ax.add_patch(rect)
          # Annotate the label
          plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
  
      # Remove the axis ticks and labels
      ax.axis('off')
  
      # Show the plot
      plt.show()
      
    image = Image.open('1.jpeg')
    parsed_answer = run_example("<OD>", image=image)
    print(parsed_answer)
    plot_bbox(image, parsed_answer["<OD>"])

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-06
• train_batch_size: 24
• eval_batch_size: 24
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.44.0.dev0
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1