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Apply computer vision research methods, models, and evaluation tools
Passed 18 of 23 test cases with the skill, and 17 of 23 without it.
2 fixed16 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
Define the fundamental input, output, and standard baseline architectures for single-image classification.
| Checked | Without skill | With skill |
|---|---|---|
| Lists ResNet, EfficientNet, ViT, and ConvNeXt for image classification. | Missed | Passed |
The instructions your model reads when the skill loads.
A skill for conducting computer vision research, covering model architectures, dataset preparation, training pipelines, evaluation metrics, and common experimental protocols for image classification, object detection, and segmentation tasks.
Image Classification:
Input: Single image
Output: Class label(s)
Models: ResNet, EfficientNet, ViT, ConvNeXt
Object Detection:
Input: Single image
Output: Bounding boxes + class labels
Models: YOLO (v5-v9), Faster R-CNN, DETR, RT-DETR
Semantic Segmentation:
Input: Single image
Output: Per-pixel class label
Models: U-Net, DeepLab, SegFormer, Mask2Former
Instance Segmentation:
Input: Single image
Output: Per-pixel labels distinguishing individual objects
Models: Mask R-CNN, Mask2Former, SAM
Image Generation:
Input: Text prompt or noise
Output: Generated image
Models: Stable Diffusion, DALL-E, Imagen
CNNs (Convolutional Neural Networks):
LeNet (1998) -> AlexNet (2012) -> VGG (2014) -> ResNet (2015)
test cases passed, vs 17 / 23 without
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