Instructions to use Nekshay/Finetuned-MobilVIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nekshay/Finetuned-MobilVIT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nekshay/Finetuned-MobilVIT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Nekshay/Finetuned-MobilVIT") model = AutoModelForImageClassification.from_pretrained("Nekshay/Finetuned-MobilVIT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": { | |
| "height": 256, | |
| "width": 256 | |
| }, | |
| "do_center_crop": true, | |
| "do_flip_channel_order": true, | |
| "do_flip_channels": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "MobileViTFeatureExtractor", | |
| "image_processor_type": "MobileViTFeatureExtractor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 288 | |
| } | |
| } | |