Instructions to use hf-internal-testing/tiny-random-TvpModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-TvpModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-TvpModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-TvpModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-TvpModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_valid_processor_keys": [ | |
| "videos", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_center_crop", | |
| "crop_size", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_pad", | |
| "pad_size", | |
| "constant_values", | |
| "pad_mode", | |
| "do_normalize", | |
| "do_flip_channel_order", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "constant_values": 0, | |
| "crop_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "do_center_crop": false, | |
| "do_flip_channel_order": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_padding": true, | |
| "do_rescale": false, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 8.2381, | |
| 7.3115, | |
| 6.6981 | |
| ], | |
| "image_processor_type": "TvpImageProcessor", | |
| "image_std": [ | |
| 9.6335, | |
| 9.0659, | |
| 8.7213 | |
| ], | |
| "pad_mode": "constant", | |
| "pad_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "padding_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "processor_class": "TvpProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 448 | |
| }, | |
| "tokenizer": "bert-base-uncased" | |
| } | |