Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/fkodom/clip-text-decoder
Generate text captions for images from their embeddings.
https://github.com/fkodom/clip-text-decoder
Last synced: 6 days ago
JSON representation
Generate text captions for images from their embeddings.
- Host: GitHub
- URL: https://github.com/fkodom/clip-text-decoder
- Owner: fkodom
- License: mit
- Created: 2021-11-11T19:22:52.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-01T14:32:41.000Z (over 1 year ago)
- Last Synced: 2024-12-13T15:16:09.552Z (18 days ago)
- Language: Python
- Homepage:
- Size: 97.7 KB
- Stars: 104
- Watchers: 2
- Forks: 7
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# clip-text-decoder
Train an image captioner with 0.323 BLEU on COCO Captions in under one hour! ([0.352 BLEU with beam search](#ablation-beam-size) 🙂)
Generates text captions for images from their embeddings. **Now includes [BLIP](https://github.com/salesforce/BLIP) as an available vision backbone!**
## Example Predictions
Computed using the pretrained model mentioned below.
"A man riding a wave on top of a surfboard."
"A baseball player is swinging a bat at a ball."
"A dog jumping in the air to catch a frisbee."
## Installation
Using `pip`:
```bash
pip install "clip @ git+https://github.com/openai/CLIP.git"
pip install "lavis @ git+https://github.com/salesforce/LAVIS.git"
pip install clip-text-decoder
```
From source:
```bash
pip install "clip @ git+https://github.com/openai/CLIP.git"
pip install "lavis @ git+https://github.com/salesforce/LAVIS.git"
git clone https://github.com/fkodom/clip-text-decoder.git
cd clip-text-decoder
pip install .
```
## Inference
### Pretrained Model
```python
from PIL import Image
import torch
from clip_text_decoder.model import ImageCaptionInferenceModel
model = ImageCaptionInferenceModel.download_pretrained()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
image = Image.open("path/to/image.jpeg")
# The beam_size argument is optional. Larger beam_size is slower, but has
# slightly higher accuracy. Recommend using beam_size <= 3.
caption = model(image, beam_size=1)
```
To cache the pretrained model locally, so that it's not re-downloaded each time:
```python
model = ImageCaptionInferenceModel.download_pretrained("path/to/model.pt")
```
### Custom Trained Model
Training produces a `model.pt` archive, containing a `Tokenizer` and model parameters. To reload the trained inference model:
```python
from clip_text_decoder.model import ImageCaptionInferenceModel
model = ImageCaptionInferenceModel.load("path/to/model.pt").to(device)
# Load image and get predictions like above...
```
## Ablation: Beam Size
Measuring the BLEU-4 score for different `beam_size` arguments. By default, the inference model uses a beam size of 1:
```python
from clip_text_decoder.model import ImageCaptionInferenceModel
model = ImageCaptionInferenceModel.load("path/to/model.pt")
caption = model(image, beam_size=1)
```
Using larger `beam_size` gives better BLEU score with a trade-off of slower inference speeds. The metrics below were collected from the same model, which uses a BLIP vision backbone and was trained for 10 epochs (roughly 1 hour on a T4 GPU):
Beam size | BLEU-4
------------|-------
1 (default) | 0.323
2 | 0.343
3 | 0.350
4 | 0.352
## Training
Launch your own training session using `train.py`:
```bash
python train.py --max-epochs 10
```
Training CLI arguments, along with their default values:
```bash
--vision-backbone blip:base # (str)
--language-model distilgpt2 # (str)
--max-epochs 10 # (int)
--beam-size 1 # (int)
--batch-size 32 # (int)
--accumulate-grad-batches 4 # (int)
--precision 16 # (16 or 32)
--seed 0 # (int)
```
One epoch takes about 5-6 minutes using a T4 GPU, which is usually free in Google Colab (depending on availability). After about 10 training epochs, you'll reach a BLEU-4 score just over 0.30 (without beam search). So, in under an hour, you can train a pretty good image captioning model. 😎
### Notes
BLEU doesn't increase much beyond 1 hour of training. Training and validation loss will continue to decrease, but the resulting image captions are effectively equivalent.
This appears to be a limitation of the image embeddings, rather than a limitation of the language model. Changing the vision backbone gives the biggest improvement in BLEU score. (BLIP gets 5-10% better BLEU than CLIP backbones using the same language model head.) Larger language models (e.g. GPT-2 Large) don't improve the BLEU score by much.
## TODO
* Plan to train on Conceptual Captions for more generic image captioning.