🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

Related tags

Text Data & NLPmachine-learningpytorchhuggingfacevision-transformers
Overview

🤗 🖼️ HuggingPics

Fine-tune Vision Transformers for anything using images found on the web.

Check out the video below for a walkthrough of this project! ⤵️

IMAGE ALT TEXT

Usage

Click on the link below to try it out:

Open In Colab

How does it work?

1. You define your search terms

pick search terms

2. We download ~150 images for each and use them to fine-tune a ViT

image search results

3. You push your model to HuggingFace's Hub to share your results with the world

push to hub

Your auto-generated model repo will look something like this. Pretty cool, eh? 😎

push to hub

Examples

💡 If you need some inspiration, take a look at the examples below:

nateraw/rare-puppers nateraw/pasta-pizza-ravioli nateraw/baseball-stadium-foods nateraw/denver-nyc-paris
term_1 samoyed pizza cotton candy denver
term_2 shiba inu pasta hamburger new york city
term_3 corgi ravioli hot dog paris
term_4 nachos
term_5 popcorn

You can see a full list of model repos created using this tool by clicking here

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Comments
  • Can't instantiate abstract class Classifier with abstract methods forward

    Can't instantiate abstract class Classifier with abstract methods forward

    Hi

    Thank you for this straight forward codes for us to practice fine-tuning models with ViT. I'm on a project that classifying book covers. Everything goes well on my own dataset until trying to define classifier. An error message popped up "Can't instantiate abstract class Classifier with abstract methods forward". As I looked up the pytorch lightning code about forward(), it is indeed an abstract class. No idea why the error since the method was initiated in the init() method.

    image

    Has anyone run into this issue yet?

    opened by e-choness 5
  • Image search returns max 35 results

    Image search returns max 35 results

    Hi,

    The image search at https://huggingface.co/api/experimental/images/search returns max 35 results. Values lower than 35 in the "count" parameter are honored; values above 35 are ignored and 35 results are returned. Your great tutorial suggests that 150 results can be gathered through this API for each query. Has the image search API changed? Thank you!

    opened by dumbshow 4
  • Issue fitting the model - RuntimeError: Found dtype Long but expected Float

    Issue fitting the model - RuntimeError: Found dtype Long but expected Float

    I'm having an issue on fitting the model. Given your example, HuggingPics works just fine. However, when I attempted to train my own model with one class with iron man, I am having issues under the Training section, cell 2, in particular

    pl.seed_everything(42)
classifier = Classifier(model, lr=2e-5)
trainer = pl.Trainer(gpus=1, precision=16, max_epochs=4)
trainer.fit(classifier, train_loader, val_loader) # ERROR HERE

    I tried to pin point the issue, but it was to no avail. First, I attempted to convert the encoding to a float in ImageClassificationCollator. However, that threw a new error for the same line,

    ValueError: The target has to be an integer tensor.

    I thought the error could be because of not enough classes, but that wasn't the case. I also thought it was because there wasn't enough data, but I I lowered the image count and your example processed fine.

    opened by Infinitay 3
  • Use latest HfApi.create_repo() parameter

    Use latest HfApi.create_repo() parameter

    Hi, it seems like HfApi.create_repo() parameters are updated and no longer treat 'name' as valid parameter. Made this PR to solve error when pushing model to huggingface hub

    TypeError: create_repo() got an unexpected keyword argument 'name'

    Screen Shot 2022-12-13 at 22 24 20 Screen Shot 2022-12-13 at 23 14 43
    opened by rizvand 0
Releases(v0.0.1)

  • v0.0.1(Nov 17, 2021)

    Add package huggingpics to PyPi, which lets you build imagefolders for anything from your local machine instead of just Colab.

    Cheers! 🍻

    Source code(tar.gz)
    Source code(zip)
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