LATAM Out-of-Distribution Few-shot Challenge
The LATAM Out-of-Distribution Few-shot Challenge is designed to push the boundaries of machine learning in autonomous driving applications. Participants will develop models that can classify unusual or specific vehicle types from minimal training data, a crucial skill in environments with unique vehicular regulations.
Challenge Description
Participants will utilize models initially trained on the ImageNet-1K dataset. The challenge involves fine-tuning these models using only six support images in a few-shot learning setup. The task is split into two distinct groups:
Easily Distinguishable Classes: For example, tuk-tuks, which are distinct from other vehicles in their appearance and function.
Sub-Groups of Common Classes: For example, fuel-transporting trucks, which require specific recognition due to unique regulatory requirements in traffic, such as maintaining a greater distance from these vehicles.
The goal is for models to effectively recognize and classify images into these specific categories with high precision, using the provided support set.
Key Challenge Details
Initial Training Data: Models will be pre-trained on the ImageNet-1K dataset.
Few-shot Learning: Fine-tuning with only six support images.
Application Focus: Autonomous driving, with emphasis on safety and regulatory compliance for specific vehicle types.
Allowed Techniques: Techniques that address out-of-distribution samples and adversarial training are permitted, provided that there's no exposure to the target domain.
Restrictions: The use of large language models (LLM) is prohibited due to the difficulty in verifying their training data domains.https://huggingface.co/spaces/Artificio/ROAM2FewShotChallenge
The LATAM Out-of-Distribution Few-shot Challenge is designed to push the boundaries of machine learning in autonomous driving applications. Participants will develop models that can classify unusual or specific vehicle types from minimal training data, a crucial skill in environments with unique vehicular regulations.
Challenge Description
Participants will utilize models initially trained on the ImageNet-1K dataset. The challenge involves fine-tuning these models using only six support images in a few-shot learning setup. The task is split into two distinct groups:
Easily Distinguishable Classes: For example, tuk-tuks, which are distinct from other vehicles in their appearance and function.
Sub-Groups of Common Classes: For example, fuel-transporting trucks, which require specific recognition due to unique regulatory requirements in traffic, such as maintaining a greater distance from these vehicles.
The goal is for models to effectively recognize and classify images into these specific categories with high precision, using the provided support set.
Key Challenge Details
Initial Training Data: Models will be pre-trained on the ImageNet-1K dataset.
Few-shot Learning: Fine-tuning with only six support images.
Application Focus: Autonomous driving, with emphasis on safety and regulatory compliance for specific vehicle types.
Allowed Techniques: Techniques that address out-of-distribution samples and adversarial training are permitted, provided that there's no exposure to the target domain.
Restrictions: The use of large language models (LLM) is prohibited due to the difficulty in verifying their training data domains.https://huggingface.co/spaces/Artificio/ROAM2FewShotChallenge
huggingface.co
ROAM2FewShotChallenge - a Hugging Face Space by Artificio
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