Instructions to use hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.11 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification/resolve/d5d4394199e65575a39a71964cfe3526f198b4f3/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification@d5d4394199e65575a39a71964cfe3526f198b4f3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification/resolve/d5d4394199e65575a39a71964cfe3526f198b4f3/pytorch_model.bin
1.11 MB
- Xet hash:
- 3f6a82c6853d7a071db1cba25f05df7bd49c33a63849352019d10a5f89a4da33
- Size of remote file:
- 1.11 MB
- SHA256:
- b56b72e27d243c1380d30162671a787034f861d1967a6a74e7fa5c0c44c96c4d
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