semvec/pypi-packages huggingface dataset:
autodistill-remote-clip 0.1.2
Remote CLIP model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill RemoteCLIP Module

This repository contains the code supporting the RemoteCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[RemoteCLIP](https://github.com/ChenDelong1999/RemoteCLIP) is a vision-language CLIP model trained on remote sensing data. According to the RemoteCLIP README:

> RemoteCLIP outperforms previous SoTA by 9.14% mean recall on the RSICD dataset and by 8.92% on RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [RemoteCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/remoteclip/).

## Installation

To use RemoteCLIP with autodistill, you need to install the following dependency:

```bash
pip3 install autodistill-remote-clip
```

## Quickstart

```python
from autodistill_remote_clip import RemoteCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our RemoteCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = RemoteCLIP(
    ontology=CaptionOntology(
        {
            "airport runway": "runway",
            "countryside": "countryside",
        }
    )
)

predictions = base_model.predict("runway.jpg")

print(predictions)
```

## License

This Autodistill module is licensed under an MIT license. At the time of publishing this project, the RemoteCLIP model and weights had no attached license. Refer to the [RemoteCLIP repository](https://github.com/ChenDelong1999/RemoteCLIP) for the most up-to-date licensing information regarding the model.

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
autodistill-altclip 0.1.2
AltCLIP model for use with Autodistill.
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill AltCLIP Module

This repository contains the code supporting the AltCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[AltCLIP](https://arxiv.org/abs/2211.06679v2) is a multi-modal vision model. With AltCLIP, you can compare the similarity between text and images, or the similarlity between two images. AltCLIP was trained on multi-lingual text-image pairs, which means it can be used for zero-shot classification with text prompts in different languages. [Read the AltCLIP paper for more information](https://arxiv.org/pdf/2211.06679v2.pdf).

The Autodistill AltCLIP module enables you to use AltCLIP for zero-shot classification.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [CLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/clip/).

## Installation

To use AltCLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-altclip
```

## Quickstart

```python
from autodistill_altclip import AltCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our AltCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated results
# then, load the model
base_model = AltCLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("construction.jpg")

print(results)
```

## License

The AltCLIP model is licensed under an [Apache 2.0 license](LICENSE). See the [model README](https://github.com/FlagAI-Open/FlagAI/blob/master/examples/AltCLIP/README.md) for more information.

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
autodistill-clip 0.1.5
CLIP module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill CLIP Module

This repository contains the code supporting the CLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[CLIP](https://github.com/openai/CLIP), developed by OpenAI, is a computer vision model trained using pairs of images and text. You can use CLIP with autodistill for image classification.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [CLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/clip/).

## Installation

To use CLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-clip
```

## Quickstart

```python
from autodistill_clip import CLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our CLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = CLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("./context_images/test.jpg")

print(results)

base_model.label("./context_images", extension=".jpeg")
```

## License

The code in this repository is licensed under an [MIT license](LICENSE.md).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
reboundp 0.0.3
A package for managing parallelized Python-based REBOUND instances
Requires Python: >=3.8

##  REBOUNDp(arallel)

#### A package to parallelize and manage parallelized Python `REBOUND` instances
---
**Version 0.0.1**

 - [x] Initiate parallel `REBOUND` instances
 - [x] Progressbar
 - [x] Access `REBOUND` ports
 - [x] Pause, unpause, and stop parallel instances
 - [x] Fetch simulations from ports
 - [ ] Separate server to manage parallelization
 - [ ] Dashboard showing CPU usage
 - [ ] Dashboard visualizing running simulations
---
**Quick start**: run a simulation in parallel
```
from reboundp import ReboundParallel

@ReboundParallel
def setup_sim(ecc):
   sim = rebound.Simulation()
   sim.add(m=1)
   sim.add(m=1e-3,  a=1,  e=ecc)
   sim.integrate(1e5)
   return sim

ecc_arr = numpy.linspace(0, 0.999, 300)
# run in parallel using all cores
results = setup_sim.run(ecc_arr, progressbar=True)
```
This comes with a nice progressbar:
```
> Progress: [==================================================] 100.0% [300/300 Tasks] [13.5s]
```
---
**Another usage**: run simulations many times.
```
from reboundp import ReboundParallel

@ReboundParallel
def setup_sim(ecc):
   sim = rebound.Simulation()
   sim.add(m=1)
   sim.add(m=1e-3,  a=1,  e=ecc)
   sim.integrate(1e5)
   return sim

# run setup_sim 10 times in parallel using 10 cores
results = setup_sim.run(jobs=10, cores=10)
```
---
***Advanded usage***: run a simulation in parallel, but now we can start/stop/retrieve simulation.
```
def setup_sim(port, _):
   sim = rebound.Simulation()
   sim.add("Solar System")
   
   # IMPORTANT: must enable server to start/stop retrieve simulations
   sim.start_server(port=port)
   
   t_arr = np.linspace(0,  1e4,  100)
   jupiter_xyz = []

   for t in t_arr:
      sim.integrate(t,  exact_finish_time=0)
      if sim.t < t: break # exit early if ended early
      jupiter_xyz.append(sim.particles[5].xyz)

   return sim, sim_id, jupiter_xyz

import threading

# run setup_sim 50 times
jobs = numpy.arange(0, 50, 1)

# run using 10 cores
parallel = ReboundParallel(simfunc = setup_sim, cores=10)

# delayed execution: pause all sims after 0.5 seconds of running
threading.Timer(0.5, parallel.pause_all).start()

# delayed execution: stop all sims after 5 seconds
threading.Timer(5., parallel.end_all).start()

# run now: parallel run
results = parallel.run(jobs=jobs)
```
autodistill-metaclip 0.1.3
MetaCLIP base model for use with Autodistill.
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill MetaCLIP Module

This repository contains the code supporting the MetaCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[MetaCLIP](https://github.com/facebookresearch/MetaCLIP), developed by Meta AI Research, is a computer vision model trained using pairs of images and text. The model was described in the [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) paper. You can use MetaCLIP with autodistill for image classification.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [MetaCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/metaclip/).

## Installation

To use MetaCLIP with autodistill, you need to install the following dependency:

```bash
pip3 install autodistill-metaclip
```

## Quickstart

### get predictions

```python
from autodistill_metaclip import MetaCLIP

# define an ontology to map class names to our MetaCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = MetaCLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("./image.png")
print(results)
```

### calculate and compare embeddings

```python
from autodistill_metaclip import MetaCLIP

base_model = MetaCLIP(None)

text = base_model.embed_text("coffee")
image = base_model.embed_image("coffeeshop.jpg")

print(base_model.compare(text, image))
```

## License

This project was licensed under a Creative Commons [Attribution-NonCommercial 4.0 International](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!