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---
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
|