datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
caltech-ucsd-birds-200-2011
Caltech-UCSD Birds-200-2011 (CUB-200-2011)
This dataset contains the Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset, from here.
Each example consists of an image, a label, and a bounding box. (The dataset also contains x/y locations of "parts", e.g. beak, right eye, left wing, throat, etc. and "attributes", e.g. beak shape, wing color, feather pattern. I have not included either of these. Contact me if you want me to add them.)
Note: Some of these images are also in ImageNet!… See the full description on the dataset page: https://huggingface.co/datasets/bentrevett/caltech-ucsd-birds-200-2011.caltech101
Dataset Card for Caltech 101
This dataset contains images of objects from 101 distinct categories, with each category comprising approximately 40 to 800 images. The majority of categories include around 50 images each. The images were collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc’Aurelio Ranzato. Each image has an approximate resolution of 300 x 200 pixels.
Dataset Sources
Website: https://data.caltech.edu/records/mzrjq-6wc02
Use in FL… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/caltech101.Caltech101
Caltech101
An MTEB dataset
Massive Text Embedding Benchmark
Classifying images of 101 widely varied objects.
Task category
i2c
Domains
Encyclopaedic
Reference
https://ieeexplore.ieee.org/document/1384978
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["Caltech101"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/Caltech101.Caltech-101
Dataset Card for "Caltech-101"
This is a non-official Caltech-101 dataset for fine-grained Image Classification.
Since there is no official method for separating training and test data, we arbitrarily split the data similar to TensorFlow.If you want to download the official dataset, please refer to the here.