Instructions to use nlpconnect/vit-gpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpconnect/vit-gpt2-image-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") model = AutoModelForImageTextToText.from_pretrained("nlpconnect/vit-gpt2-image-captioning") - Notebooks
- Google Colab
- Kaggle
Update README.md
#8
by itsog - opened
README.md
CHANGED
|
@@ -29,6 +29,8 @@ This is an image captioning model trained by @ydshieh in [flax ](https://github.
|
|
| 29 |
```python
|
| 30 |
|
| 31 |
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
|
|
|
|
|
|
|
| 32 |
|
| 33 |
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
|
| 34 |
feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
|
|
|
|
| 29 |
```python
|
| 30 |
|
| 31 |
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
|
| 32 |
+
import torch
|
| 33 |
+
from PIL import Image
|
| 34 |
|
| 35 |
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
|
| 36 |
feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
|