Practical overview
When it fits
Use it when you want a task-specific notebook example for a vision model in Hugging Face Transformers, especially for object detection inference or custom-dataset fine-tuning.
Who it is for
- Developers exploring computer-vision models through notebook examples.
- Practitioners looking for object-detection inference or custom-dataset fine-tuning examples.
- Learners comparing example workflows across vision tasks such as depth or pose estimation.
Documented uses
- Run an example workflow for object-detection inference.
- Explore fine-tuning an object-detection model on a custom dataset.
- Find notebook examples for depth estimation, pose estimation, keypoint matching, image classification or zero-shot object detection.
Requirements & limits
The repository is public and licensed under Apache-2.0. Many listed notebooks link to Google Colab.
- Choose by task and model: the README lists inference and fine-tuning separately where both are available.
- The collection includes tracking for D-Fine, while other listed models have examples for their stated tasks.
- The README does not specify a shared environment setup or hardware requirements; inspect the selected notebook for its own details.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Open a vision-model inference notebook in Colab
Use a linked notebook for inference with a listed vision model.
Before you start
- Access to the repository README and its linked notebook or Colab page.
Steps
Choose a model and inference task from the README table.
Open that model’s linked inference notebook, or select its corresponding Open in Colab link.
Follow the instructions in the notebook to carry out the inference workflow.
Expected outcome
A notebook is open for the selected model’s inference workflow.
Guide limitations
- The README lists available notebooks but does not describe their cell-by-cell execution steps or expected results.
Open a custom-dataset object-detection fine-tuning notebook
Start from a listed notebook for fine-tuning an object-detection model on a custom dataset.
Before you start
- Access to the repository README and its linked notebook or Colab page.
- A custom dataset, as indicated by the notebook’s purpose; dataset format and preparation requirements are not specified in the README.
Steps
Choose a listed object-detection model with a fine-tuning notebook, such as RT-DETRv2, DAB-DETR, or D-Fine.
Open its fine-tuning notebook from the README, or select the corresponding Open in Colab link.
Consult the notebook for the fine-tuning workflow and dataset-specific steps.
Expected outcome
A custom-dataset fine-tuning notebook is open for the selected object-detection model.
Guide limitations
- The README does not specify dataset format, preparation, training settings, or expected results.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- qubvel/transformers-notebooks · Official homepage title · retained in its source language