---
license: mit
base_model: microsoft/Florence-2-large-ft
tags:
- image-to-text
- generated_from_trainer
model-index:
- name: Florence-2-large-FormClassification-ft
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Florence-2-large-FormClassification-ft

This model is a fine-tuned version of [microsoft/Florence-2-large-ft](https://huggingface.co/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
```python
  # 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

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0188        | 1.0   | 23   | 0.2151          |
| 0.0127        | 2.0   | 46   | 0.2113          |
| 0.0078        | 3.0   | 69   | 0.2061          |
| 0.0047        | 4.0   | 92   | 0.2102          |
| 0.0042        | 5.0   | 115  | 0.2078          |
| 0.003         | 6.0   | 138  | 0.2108          |
| 0.0022        | 7.0   | 161  | 0.2110          |
| 0.0029        | 8.0   | 184  | 0.2117          |
| 0.0019        | 9.0   | 207  | 0.2114          |
| 0.0023        | 10.0  | 230  | 0.2107          |


### Framework versions

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