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--- |
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license: mit |
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base_model: microsoft/Florence-2-large-ft |
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tags: |
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- image-to-text |
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- generated_from_trainer |
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model-index: |
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- name: Florence-2-large-FormClassification-ft |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Florence-2-large-FormClassification-ft |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2107 |
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### Inference Code |
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```python |
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# Code |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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model = AutoModelForCausalLM.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True, device_map='cuda') # Load the model on GPU if available |
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processor = AutoProcessor.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True) |
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def run_example(task_prompt, image, max_new_tokens=128): |
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prompt = task_prompt |
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inputs = processor(text=prompt, images=image, return_tensors="pt") |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"].cuda(), |
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pixel_values=inputs["pixel_values"].cuda(), |
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max_new_tokens=max_new_tokens, |
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early_stopping=False, |
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do_sample=False, |
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num_beams=3, |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = processor.post_process_generation( |
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generated_text, |
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task=task_prompt, |
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image_size=(image.width, image.height) |
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) |
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return parsed_answer |
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def plot_bbox(image, data): |
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fig, ax = plt.subplots() |
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# Display the image |
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ax.imshow(image) |
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# Plot each bounding box |
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for bbox, label in zip(data['bboxes'], data['labels']): |
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# Unpack the bounding box coordinates |
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x1, y1, x2, y2 = bbox |
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# Create a Rectangle patch |
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rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none') |
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# Add the rectangle to the Axes |
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ax.add_patch(rect) |
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# Annotate the label |
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plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5)) |
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# Remove the axis ticks and labels |
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ax.axis('off') |
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# Show the plot |
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plt.show() |
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image = Image.open('1.jpeg') |
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parsed_answer = run_example("<OD>", image=image) |
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print(parsed_answer) |
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plot_bbox(image, parsed_answer["<OD>"]) |
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``` |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.0188 | 1.0 | 23 | 0.2151 | |
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| 0.0127 | 2.0 | 46 | 0.2113 | |
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| 0.0078 | 3.0 | 69 | 0.2061 | |
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| 0.0047 | 4.0 | 92 | 0.2102 | |
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| 0.0042 | 5.0 | 115 | 0.2078 | |
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| 0.003 | 6.0 | 138 | 0.2108 | |
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| 0.0022 | 7.0 | 161 | 0.2110 | |
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| 0.0029 | 8.0 | 184 | 0.2117 | |
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| 0.0019 | 9.0 | 207 | 0.2114 | |
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| 0.0023 | 10.0 | 230 | 0.2107 | |
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### Framework versions |
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- Transformers 4.44.0.dev0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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