each task that were best suited to addressing your query, based on their training and performance characteristics.For image-to-text, I chose the        nlpconnect/vit-gpt2-image-captioning model. This model generates an image caption with the given image. I applied this model to the input image and the developed output was: a family of four dogs are playing in the grassFor image-classification, I selected the        google/vit-base-patch16-224 model. This model is trained on natural images dataset and it can predict the label of the image output. I applied this model to the image and get the results showing the risk of each label. It shows the highest risk at "Rhodesian ridgeback" with a score of 93.8%For object-detection, I used        facebook/detr-resnet-101 model. This model is trained on COCO panoptic segmentation for object detection. I used this model to generate an image file with the predicted box. The generated image with predicted box is at:        /images/sd48.jpgRegarding
HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face