|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: microsoft/git-base |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: git-base-lucy1 |
|
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. --> |
|
|
|
# git-base-lucy1 |
|
|
|
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 5.9368 |
|
- Wer Score: 3.1310 |
|
|
|
## Model description |
|
|
|
Fine-tuned captioning model on Lucy in the Sky images. |
|
|
|
Dataset: [tonyassi/lucy-caption-2](https://huggingface.co/datasets/tonyassi/lucy-caption-2) |
|
|
|
## Usage |
|
```python |
|
import torch |
|
from PIL import Image |
|
from transformers import AutoProcessor, AutoModelForCausalLM |
|
import requests |
|
|
|
# Load model directly |
|
processor = AutoProcessor.from_pretrained("tonyassi/git-base-lucy1") |
|
model = AutoModelForCausalLM.from_pretrained("tonyassi/git-base-lucy1") |
|
|
|
# Load image |
|
url = "https://datasets-server.huggingface.co/cached-assets/tonyassi/lucy-caption-2/--/94d2ffc965a7a0a50beebbeb60d04fa38a24ff66/--/default/train/6/image/image.jpg?Expires=1727109954&Signature=IMpoIBQV-ICPaC8V4NF2SUn0OQE7cOtIJZIeGSpOQLifkjlXl3rx6CAukc2Ax3Gkl4eQ3BfcDrnV2HNzE-c3c5WC5lcj30PWTkSczcqN7YtkKGFHOxlS6-Gv8zotQw8NJPn0d-LoCERHlxA75Sbh8vF8X6DE1SCRJIitT3DFcObTdKpZpHYDv21BYq4-A4EN04wX6aKHWyz8xR0NorlOtcB8dzPdsSpRGy3gcgLU9kHeBNWpa22KsMDJmDP8QferzrnG5bnb5fi9RxrMCTURCPUB8AyNJ1mVwuAorW4GJIdm40UEoqanQzrST3hIp-dTEH47w5-GY5PnOrWUcaxYGQ__&Key-Pair-Id=K3EI6M078Z3AC3" |
|
image = Image.open(requests.get(url, stream=True).raw) |
|
|
|
# GPU or CPU |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
|
# Inference |
|
inputs = processor(images=image, return_tensors="pt").to(device) |
|
pixel_values = inputs.pixel_values |
|
generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
|
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
|
print(generated_caption) |
|
|
|
``` |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:| |
|
| 3.589 | 50.0 | 50 | 5.9368 | 3.1310 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
