|
--- |
|
title: TSAI S19 |
|
emoji: 🏆 |
|
colorFrom: yellow |
|
colorTo: blue |
|
sdk: gradio |
|
sdk_version: 3.45.2 |
|
app_file: app.py |
|
pinned: false |
|
license: mit |
|
--- |
|
|
|
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
|
|
|
# The School of AI - ERA(Extensive & Reimagined AI Program) - Assignment 19 |
|
|
|
This folder consists of Assignment-19 from ERA course offered by - TSAI(The school of AI). |
|
Follow https://theschoolof.ai/ for more updates on TSAI |
|
|
|
Assignment-19 |
|
Make a CLIP or FastSAM application on gradio/spaces using open-source models. |
|
- You may use the open source pre-trained models for CLIP or FastSAM |
|
|
|
### Implementation and Results: |
|
Implemented a simple gradio interface on higgingface and Github. Used a pretrained CLIP model from https://github.com/openai/CLIP. Model has been pretrained on Coco Dataset |
|
|
|
HF Link: https://huggingface.co/spaces/ToletiSri/TSAI_S19 |
|
Github Link: https://github.com/ToletiSri/TSAI_ERA_Assignments/tree/main/S19 |
|
|
|
Add any image that belongs to one of the 80 Coco datasets, in the input image box |
|
Add some captions for this image. The results(output text box) shown are the likelihood percentage that the image belongs to one of these captions. |
|
|
