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A simple terrain recognition that i did to cement the lesson for lesson one and two
The notebook: terrain | Kaggle
hugging face space: Terrain - a Hugging Face Space by Mekbib
I tried using the gradio api to create a web interface but it wasn’t working quite well so i just embeded the hugging face space on my page: Terrain recognition

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Hi all! After taking the lesson 1. I’ve trained a model to determinate if a tire’s rubber is warn or not:

Notebook: [Checking the condition of a tire | Kaggle](https://tire’s rubber notebook)

As you can see the image, something that has not been clear for me is that if you enter more than one word in the image search, the model can categorize by words, not the entire search.

After train the model, I think the error_rate is somewhat high, but it works quite well for being my first training of a model.

Captura de pantalla 2024-03-27 a las 13.04.06


Hello everyone, After first lesson, I trained a model to recognize if a photo is of cheetah or leopard.
Here is the kaggle notebook link: leopard_or_cheetah | Kaggle

I got pretty good loss values, but the model still make some mistakes


Hi Folks!
I have completed Lecture 2 Assignment. The project I chose was classifying the paintings made by Picasso and Monet (I couldn’t think of any crazy project :worried:). Deployed it in HuggingFace Spaces and also wrote a blog (setup my blog using Quarto & Github Pages) about the project with source code. Happy feelings!

Blog: Harish B - Picasso and Monet Painting Classifier (
App: Painting Classifier (


Hello Fastai Fam!

I’m excited to share my latest project with you all: a pet and predator classifier! :paw_prints: Check it out
Together we grow!! Looking forward to your feedback

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Hello, my friends,

It’s great joy for me being part of this community.

Tonight (over here), I’m glad to share that with the understanding gotten from the course taught by @jeremy and greatly referencing the “Is it a bird?” notebook, I’ve been able to build a smooth “Is there a roadblock ahead?” computer vision model that is greatly able to determine if there’s a roadblock ahead in no time, both at night and day.
The model which is fine-tuned on a dataset of “roadblock and free road photos” uses the pre-trained “resnet18” model.

Resnet18 did amazingly well and Kaggle is so kind with the free GPUs offering of 30hrs. Lol, and I’m just getting started. Cheers.

Please, my friends, I’d appreciate your comments and feedback on this.
Thanks, here’s the link; Is there a roadblock ahead?


Very nice.
Sai Baba answers

Hi @Salman_Ahmad! The link is not working.

I observed behaviour as well that indicated alphabetic sorting.

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Hey everyone! :star2: I’m new here but excited to be part of the community. I’ve got a unique challenge and I’m hoping to get some advice on which Fastai model might be the best fit. I’m working with structured spreadsheet data to explore the relationship between food intake (with details like macros) and bowel movements, trying to identify trigger foods. The twist is the delayed reaction - up to 48 hours between eating and the effects. So, it’s not straightforward.

Here’s the lowdown:

  • Data: Two tables. One with food and nutrient intake, and another describing bowel movements (e.g., thickness, fragmentation).
  • Objective: Predict ‘good’ vs ‘bad’ bowel movements based on food intake, accounting for the up to 48-hour delay. Ideally this is a Regression model with a scale of the degree of fragmentation, thickness, etc. But to start a simple binary “good” or “bad” would suffice.
  • Data Size: 6 months worth.

Given the delay in reaction and the structured nature of my data, I’m scratching my head on how to model this. Any suggestions on models or a particular approach within fastai that could handle this delayed effect scenario effectively?

Appreciate all your insights and recommendations!