hugging-face-vision-trainer

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2. Use your finetuned model for inference. To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/object-detection) Before you begin, make sure you have all the necessary libraries installed: ```bash pip install -q datasets transformers accelerate timm trackio pip install -q -U albumentations>=1.4.5 torchmetrics pycocotools ``` You'll use 🤗 Datasets to load a dataset from the Hugging Face Hub, 🤗 Transformers to train

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### Job timeout Increase timeout (see directive #5 table), reduce epochs/dataset, or use checkpoint strategy with `hub_strategy="every_save"`.

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Load an image you'd like to run inference on: ```py >>> ds = load_dataset("ethz/food101", split="validation[:10]") >>> image = ds["image"][0] ``` The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](/docs/transformers/v5.2.0/en/main_classes/pipelines#transformers.pipeline).

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