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YOLOExplorer

Explore, manipulate and iterate on Computer Vision datasets with precision using simple APIs. Supports SQL filters, vector similarity search, native interface with Pandas and more.

• Analyse your datasets with powerful custom queries
• Find and remove bad images (duplicates, out of domain data and more)
• Enrich datasets by adding more examples from another datasets
• And more

🌟 NEW: Supports GUI Dashboard, Pythonic and notebook workflows

Dashboard Workflows

exp = Explorer("VOC.yaml") exp.build_embeddings()

coco_exp = Explorer("coco128.yaml") coco_exp.build_embeddings() #Init coco8 similarly

exp.dash([coco_exp, coco8]) #Automatic analysis coming soon with dash(..., analysis=True)

You can now explore multiple pretrained models listed "resnet18", "resnet50", "efficientnet_b0", "efficientnet_v2_s", "googlenet", "mobilenet_v3_small" for extracting better features out of images to improve searching across multiple datasets.<pre> from yoloexplorer import Explorer

exp = Explorer("coco128.yaml", model="resnet50") exp.build_embeddings()

coco_exp = Explorer("coco128.yaml", model="mobilenet_v3_small") coco_exp.build_embeddings()

#Use force=True as a parameter in build_embedding if embeddings already exists

exp.dash([coco_exp, coco8]) #Automatic analysis coming soon with dash(..., analysis=True)

Installation

pip install yoloexplorer

Install from source branch

pip install git+https://github.com/lancedb/yoloexplorer.git

Pypi installation coming soon

Quickstart

YOLOExplorer can be used to rapidly generate new versions of CV datasets trainable on Ultralytics YOLO, SAM, FAST-SAM, RT-DETR and more models.

Start exploring your Datasets in 2 simple steps

• Select a supported dataset or bring your own. Supports all Ultralytics YOLO datasets currently

from yoloexplorer import Explorer

coco_exp = Explorer("coco128.yaml")

• Build the LanceDB table to allow querying

coco_exp.build_embeddings()
coco_exp.dash() # Launch the GUI dashboard

You can get the schema of you dataset once the table is built

schema = coco_exp.table.schema

You can use this schema to run queries

<b>SQL query</b><br/> Let's try this query and print 4 result - Select instances that contain one or more 'person' and 'cat'

df = coco_exp.sql("SELECT * from 'table' WHERE labels like '%person%' and labels LIKE '%cat%'")
coco_exp.plot_imgs(ids=df["id"][0:4].to_list())

Result

<img src="./yoloexplorer/assets/docs/plotting.png" height=50% width=50% /><br/> The above is equivlant to plotting directly with a query:

voc_exp.plot_imgs(query=query, n=4)

<b>Querying by similarity</b><br/> Now lets say your model confuses between cetain classes( cat & dog for example) so you want to look find images similar to the ones above to investigate.

The id of the first image in this case was 117

imgs, ids = coco_exp.get_similar_imgs(117, n=6) # accepts ids/idx, Path, or img blob
voc_exp.plot_imgs(ids)

<img src="./yoloexplorer/assets/docs/sim_plotting.png" height=50% width=50% /><br/> The above is equivlant to directly calling plot_similar_imgs

voc_exp.plot_similar_imgs(117, n=6)

NOTE: You can also pass any image file for similarity search, even the ones that are not in the dataset

<b>Similarity Search with SQL Filter (Coming Soon)</b></br> Soon you'll be able to have a finer control over the queries by pre-filtering your table

coco_exp.get_similar_imgs(..., query="WHERE labels LIKE '%motorbike%'")
coco_exp.plot_similar_imgs(query="WHERE labels LIKE '%motorbike%'")

Additional Details

• The plot_imgs method can be used to visualize a subset of images from the dataset. The ids argument can be a list of image IDs, or a SQL query that returns a list of image IDs. The n argument specifies the number of images to plot.
• The plot_similar_imgs method can be used to visualize the top n similar images to a given image. The img/idx argument can be the index of the image in the dataset, the path to the image file, or the encoded/binary representation of the image.
• The plot_similarity_index method can be used to visualize the similarity index of the dataset. The similarity index is a measure of how similar each image is to all the other images in the dataset. The top_k argument specifies the percentage of images to keep for the similarity index. The sim_thres argument specifies the similarity threshold. The reduce argument specifies whether to reduce the dimensionality of embeddings before calculating the index. The sorted argument specifies whether to sort the similarity index.

<b>Removing data</b><br/> You can simply remove images by passing a list of ids from the table.

coco_exp.remove_imgs([100,120,300..n]) # Removes images at the given ids.

<b>Adding data</b><br/> For adding data from another dataset, you need an explorer object of that dataset with embeddings built. You can then pass that object along with the ids of the imgs that you'd like to add from that dataset.

coco_exp.add_imgs(exp, idxs) #

Note: You can use SQL querying and/or similarity searches to get the desired ids from the datasets.

<b>Persisting the Table: Create new dataset and start training</b><br/> After making the desired changes, you can persist the table to create the new dataset.

coco_exp.persist()

This creates a new dataset and outputs the training command that you can simply paste in your terminal to train a new model!

<b>Resetting the Table</b><br/> You can reset the table to its original or last persisted state (whichever is latest)

coco_exp.reset()

voc_exp.plot_similarity_index()

<img src="./yoloexplorer/assets/docs/sim_index.png" height=50% width=50%><br/> You can also get the the similarity index as a numpy array to perform advanced querys.

sim = voc_exp.get_similarity_index()

Now you can combine the similarity index with other querying options discussed above to create even more powerful queries. Here's an example:

"Let's say you've created a list of candidates you wish to remove from the dataset. Now, you want to filter out the images that have similarity index less than 250, i.e, remove the images that are 90%(sim_thres) or more similar to more than 250 images in the dataset. "

ids = [...] # filtered ids list
filter = np.where(sim > 250)
final_ids = np.intersect1d(ids, filter) # intersect both arrays

exp.remove_imgs(final_ids)

<b>Pre-filtering</b>

• To allow adding filter to searches.
• Have a finer control over embeddings search space

Pre-filtering will enable powerful queries like - "Show me images similar to <IMAGE> and include only ones that contain one or more(or exactly one) person, 2 cars and 1 horse" <br/>

• <b>Automatically find potential duplicate images</b>

• <b>Better embedding plotting and analytics insights </b>

• <b>Better dashboard for visualizing imgs </b>

  </br>

Notes:

• The API will have some minor changes going from dev to minor release
• For all practical purposes the ids are same as row number and is reset after every addition or removal