Image-to-Text
Transformers
PyTorch
Safetensors
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use AIris-Channel/vit-gpt2-verifycode-caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIris-Channel/vit-gpt2-verifycode-caption 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="AIris-Channel/vit-gpt2-verifycode-caption")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption") model = AutoModelForImageTextToText.from_pretrained("AIris-Channel/vit-gpt2-verifycode-caption") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |