Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SiglipForImageClassification" | |
| ], | |
| "bos_token_id": 49406, | |
| "eos_token_id": 1, | |
| "initializer_factor": 1.0, | |
| "model_type": "siglip", | |
| "pad_token_id": 1, | |
| "text_config": { | |
| "attention_dropout": 0.1, | |
| "dropout": 0.1, | |
| "eos_token_id": 1, | |
| "hidden_size": 32, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37, | |
| "max_position_embeddings": 512, | |
| "model_type": "siglip_text_model", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 2 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.0.dev0", | |
| "vision_config": { | |
| "attention_dropout": 0.1, | |
| "dropout": 0.1, | |
| "hidden_size": 32, | |
| "image_size": 30, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 2, | |
| "patch_size": 2 | |
| } | |
| } | |