| --- |
| 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 |
|
|
