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---
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: paligemma_race
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. -->
# paligemma_race
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the FairFace dataset.
``` python
from PIL import Image
import torch
from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig, TrainingArguments, Trainer
model = PaliGemmaForConditionalGeneration.from_pretrained('NYUAD-ComNets/paligemma_race',torch_dtype=torch.bfloat16)
input_text = "what is race of person in the image?"
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
input_image = Image.open('image_path')
inputs = processor(text=input_text, images=input_image, padding="longest", do_convert_rgb=True, return_tensors="pt").to(device)
inputs = inputs.to(dtype=model.dtype)
with torch.no_grad():
output = model.generate(**inputs, max_length=500)
result=processor.decode(output[0], skip_special_tokens=True)[len(input_text):].strip()
```
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.1.2+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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