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Finetuning/.gitattributes

@@ -0,0 +1,2 @@
+*.py linguist-language=python
+*.ipynb linguist-documentation

Finetuning/.gitignore

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+**/logs/
+**/wandb/
+models/
+features/
+results/
+
+tests/data/
+*.pt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+sync.sh
+gpu1sync.sh
+.idea
+*.pdf
+**/._*
+**/*DS_*
+**.jsonl
+src/sbatch
+src/misc
+.vscode
+src/debug
+core.*
+
+# Allow
+!src/evaluation/misc/results_dbs/*

Finetuning/CITATION.cff

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+cff-version: 1.1.0
+message: If you use this software, please cite it as below.
+authors:
+  - family-names: Ilharco
+    given-names: Gabriel
+  - family-names: Wortsman
+    given-names: Mitchell
+  - family-names: Wightman
+    given-names: Ross
+  - family-names: Gordon
+    given-names: Cade   
+  - family-names: Carlini
+    given-names: Nicholas
+  - family-names: Taori
+    given-names: Rohan
+  - family-names: Dave
+    given-names: Achal
+  - family-names: Shankar
+    given-names: Vaishaal
+  - family-names: Namkoong
+    given-names: Hongseok
+  - family-names: Miller
+    given-names: John
+  - family-names: Hajishirzi
+    given-names: Hannaneh
+  - family-names: Farhadi
+    given-names: Ali
+  - family-names: Schmidt
+    given-names: Ludwig
+title: OpenCLIP
+version: v0.1
+doi: 10.5281/zenodo.5143773
+date-released: 2021-07-28

Finetuning/HISTORY.md

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+## 2.24.0
+
+* Fix missing space in error message
+* use model flag for normalizing embeddings
+* init logit_bias for non siglip pretrained models
+* Fix logit_bias load_checkpoint addition 
+* Make CoCa model match CLIP models for logit scale/bias init
+* Fix missing return of "logit_bias" in CoCa.forward
+* Add NLLB-CLIP with SigLIP models
+* Add get_logits method and NLLB tokenizer
+* Remove the empty file src/open_clip/generation_utils.py
+* Update params.py: "BatchNorm" -> "LayerNorm" in the description string for "--lock-text-freeze-layer-norm"
+
+## 2.23.0
+
+* Add CLIPA-v2 models
+* Add SigLIP models
+* Add MetaCLIP models
+* Add NLLB-CLIP models
+* CLIPA train code
+* Minor changes/fixes
+    * Remove protobuf version limit
+    * Stop checking model name when loading CoCa models
+    * Log native wandb step
+    * Use bool instead of long masks
+
+## 2.21.0
+
+* Add SigLIP loss + training support
+* Add more DataComp models (B/16, B/32 and B/32@256)
+* Update default num workers
+* Update CoCa generation for `transformers>=4.31`
+* PyTorch 2.0 `state_dict()` compatibility fix for compiled models
+* Fix padding in `ResizeMaxSize`
+* Convert JIT model on state dict load for `pretrained='filename…'`
+* Other minor changes and fixes (typos, README, dependencies, CI)
+
+## 2.20.0
+
+* Add EVA models
+* Support serial worker training
+* Fix Python 3.7 compatibility 
+
+## 2.19.0
+
+* Add DataComp models
+
+## 2.18.0
+
+* Enable int8 inference without `.weight` attribute
+
+## 2.17.2
+
+* Update push_to_hf_hub
+
+## 2.17.0
+
+* Add int8 support
+* Update notebook demo
+* Refactor zero-shot classification code
+
+## 2.16.2
+
+* Fixes for context_length and vocab_size attributes 
+
+## 2.16.1
+
+* Fixes for context_length and vocab_size attributes 
+* Fix --train-num-samples logic
+* Add HF BERT configs for PubMed CLIP model
+
+## 2.16.0
+
+* Add improved g-14 weights
+* Update protobuf version
+
+## 2.15.0
+
+* Add convnext_xxlarge weights
+* Fixed import in readme
+* Add samples per second per gpu logging
+* Fix slurm example
+
+## 2.14.0
+
+* Move dataset mixtures logic to shard level
+* Fix CoCa accum-grad training
+* Safer transformers import guard
+* get_labels refactoring
+
+## 2.13.0
+
+* Add support for dataset mixtures with different sampling weights
+* Make transformers optional again 
+
+## 2.12.0
+
+* Updated convnext configs for consistency
+* Added input_patchnorm option
+* Clean and improve CoCa generation
+* Support model distillation
+* Add ConvNeXt-Large 320x320 fine-tune weights
+
+## 2.11.1
+
+* Make transformers optional
+* Add MSCOCO CoCa finetunes to pretrained models
+
+## 2.11.0
+
+* coca support and weights
+* ConvNeXt-Large weights
+
+## 2.10.1
+
+* `hf-hub:org/model_id` support for loading models w/ config and weights in Hugging Face Hub
+
+## 2.10.0
+
+* Added a ViT-bigG-14 model.
+* Added an up-to-date example slurm script for large training jobs.
+* Added a option to sync logs and checkpoints to S3 during training.
+* New options for LR schedulers, constant and constant with cooldown
+* Fix wandb autoresuming when resume is not set
+* ConvNeXt `base` & `base_w` pretrained models added
+* `timm-` model prefix removed from configs
+* `timm` augmentation + regularization (dropout / drop-path) supported
+
+## 2.9.3
+
+* Fix wandb collapsing multiple parallel runs into a single one
+
+## 2.9.2
+
+* Fix braceexpand memory explosion for complex webdataset urls
+
+## 2.9.1
+
+* Fix release
+
+## 2.9.0
+
+* Add training feature to auto-resume from the latest checkpoint on restart via `--resume latest`
+* Allow webp in webdataset
+* Fix logging for number of samples when using gradient accumulation
+* Add model configs for convnext xxlarge
+
+## 2.8.2
+
+* wrapped patchdropout in a torch.nn.Module
+
+## 2.8.1
+
+* relax protobuf dependency
+* override the default patch dropout value in 'vision_cfg'
+
+## 2.8.0
+
+* better support for HF models
+* add support for gradient accumulation
+* CI fixes
+* add support for patch dropout
+* add convnext configs
+
+
+## 2.7.0
+
+* add multilingual H/14 xlm roberta large
+
+## 2.6.1
+
+* fix setup.py _read_reqs
+
+## 2.6.0
+
+* Make openclip training usable from pypi.
+* Add xlm roberta large vit h 14 config.
+
+## 2.5.0
+
+* pretrained B/32 xlm roberta base: first multilingual clip trained on laion5B
+* pretrained B/32 roberta base: first clip trained using an HF text encoder
+
+## 2.4.1
+
+* Add missing hf_tokenizer_name in CLIPTextCfg.
+
+## 2.4.0
+
+* Fix #211, missing RN50x64 config. Fix type of dropout param for ResNet models
+* Bring back LayerNorm impl that casts to input for non bf16/fp16 
+* zero_shot.py: set correct tokenizer based on args
+* training/params.py: remove hf params and get them from model config
+
+## 2.3.1
+
+* Implement grad checkpointing for hf model.
+* custom_text: True if hf_model_name is set
+* Disable hf tokenizer parallelism 
+
+## 2.3.0
+
+* Generalizable Text Transformer with HuggingFace Models (@iejMac)
+
+## 2.2.0
+
+* Support for custom text tower
+* Add checksum verification for pretrained model weights 
+
+## 2.1.0
+
+* lot including sota models, bfloat16 option, better loading, better metrics
+
+## 1.2.0
+
+* ViT-B/32 trained on Laion2B-en
+* add missing openai RN50x64 model
+
+## 1.1.1
+
+* ViT-B/16+
+* Add grad checkpointing support
+* more robust data loader

Finetuning/LICENSE

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+Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, 
+Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, 
+John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, 
+Ludwig Schmidt
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Finetuning/MANIFEST.in

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+include src/open_clip/bpe_simple_vocab_16e6.txt.gz
+include src/open_clip/model_configs/*.json
+

Finetuning/Makefile

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+install: ## [Local development] Upgrade pip, install requirements, install package.
+	python -m pip install -U pip
+	python -m pip install -e .
+
+install-training:
+	python -m pip install -r requirements-training.txt
+
+install-test: ## [Local development] Install test requirements
+	python -m pip install -r requirements-test.txt
+
+test: ## [Local development] Run unit tests
+	python -m pytest -x -s -v tests

Finetuning/README.md

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+# OpenCLIP
+
+This is a fork of <a href="https://github.com/mlfoundations/open_clip">OpenCLIP</a> used to fine-tune CLIP models with PinPoint counterfactuals. Refer to the original repository for more details on open_clip.
+
+
+### Installation
+
+```
+pip install open_clip_torch
+```
+
+
+### Pretrained models
+
+For LAION-pretrained models, download and  place them in the ./pretrained_models (this can be done with open_clip CLI interface)/
+
+### Sample single-process running code:
+
+To finetune CLIP models on CC3M:
+
+```bash
+python -m open_clip_train.main \
+    --save-frequency 1 \
+    --zeroshot-frequency 1 \
+    --report-to tensorboard \
+    --train-data="..path_to_image_list.csv" \
+    --csv-img-key="Image_ID" \
+    --csv-caption-key="Caption" \
+    --val-data="/path/to/validation_data.csv"  \
+    --imagenet-val="/path/to/imagenet/root/val/" \
+    --warmup 10000 \
+    --batch-size=128 \
+    --accum_freq=10 \
+    --lr=5e-06 \
+    --wd=0.1 \
+    --epochs=410 \
+    --workers=8 \
+    --pretrained_model="pretrained_models/vit_b16_laion2b.pth" \
+    --model ViT-B-16
+```
+
+Note: `imagenet-val` is the path to the *validation* set of ImageNet for zero-shot evaluation, not the training set!
+You can remove this argument if you do not want to perform zero-shot evaluation on ImageNet throughout training. Note that the `val` folder should contain subfolders. If it does not, please use [this script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh).
+
+Note: the `train_data` should point to a *.csv file that contains the filelist with generated images in the following format:
+`ÌMAGE_ID   IMAGE_CAPTION`, separated by '\t'. You can find the lists for our in-painted data under `./annotations`.
+

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Finetuning/docs/Interacting_with_open_coca.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install open_clip_torch transformers"
+      ],
+      "metadata": {
+        "id": "JvaEkx8Cyvhg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "vE4lFFkKyotX"
+      },
+      "outputs": [],
+      "source": [
+        "import open_clip\n",
+        "import torch\n",
+        "\n",
+        "model, _, transform = open_clip.create_model_and_transforms(\n",
+        "  model_name=\"coca_ViT-L-14\",\n",
+        "  pretrained=\"mscoco_finetuned_laion2B-s13B-b90k\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg"
+      ],
+      "metadata": {
+        "id": "oOaE1AmDyth_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import Image\n",
+        "Image('cat.jpg')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "id": "Y9Q6bhVA2L01",
+        "outputId": "1b920080-e8cd-4d2f-fb23-30e6f1b612f9"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/jpeg": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from PIL import Image\n",
+        "im = Image.open(\"cat.jpg\").convert(\"RGB\")\n",
+        "im = transform(im).unsqueeze(0)\n",
+        "\n",
+        "with torch.no_grad(), torch.cuda.amp.autocast():\n",
+        "  generated = model.generate(im)\n",
+        "\n",
+        "print(open_clip.decode(generated[0]).split(\"<end_of_text>\")[0].replace(\"<start_of_text>\", \"\"))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "byZKXMGzyr5Y",
+        "outputId": "122eb099-6704-4e3c-fa7c-a05dd87ce64f"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "an orange and white cat on top of a turtle . \n"
+          ]
+        }
+      ]
+    }
+  ]
+}

Finetuning/docs/LOW_ACC.md

@@ -0,0 +1,38 @@
+As we describe in more detail below, CLIP models in a medium accuracy regime already allow us to draw conclusions about the robustness of larger CLIP models since the models follow reliable scaling laws.
+
+[Cherti et al., 2022](https://arxiv.org/abs/2212.07143) and [Gadre et al., 2023](https://arxiv.org/abs/2304.14108) show additional discussions about the scaling behavior of CLIP models.
+
+## Scaling trends
+
+The plot below shows how zero-shot performance of CLIP models varies as we scale the number of samples used for training. Zero-shot performance increases steadily for both ImageNet and [ImageNetV2](https://arxiv.org/abs/1902.10811), and is far from saturated at ~15M samples.
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/scaling.png" width="700">
+
+## Why are low-accuracy CLIP models interesting?
+
+**TL;DR:** CLIP models have high effective robustness, even at small scales.
+
+CLIP models are particularly intriguing because they are more robust to natural distribution shifts (see Section 3.3 in the [CLIP paper](https://arxiv.org/abs/2103.00020)).
+This phenomena is illustrated by the figure below, with ImageNet accuracy on the x-axis
+and [ImageNetV2](https://arxiv.org/abs/1902.10811) (a reproduction of the ImageNet validation set with distribution shift) accuracy on the y-axis.
+Standard training denotes training on the ImageNet train set and the CLIP zero-shot models
+are shown as stars.
+
+![CLIP scatter plot](https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/effective_robustness.png)
+
+As observed by [Taori et al., 2020](https://arxiv.org/abs/2007.00644) and [Miller et al., 2021](https://arxiv.org/abs/2107.04649), the in-distribution
+and out-of-distribution accuracies of models trained on ImageNet follow a predictable linear trend (the red line in the above plot). *Effective robustness*
+quantifies robustness as accuracy beyond this baseline, i.e., how far a model lies above the red line. Ideally a model would not suffer from distribution shift and fall on the y = x line ([trained human labelers are within a percentage point of the y = x line](http://proceedings.mlr.press/v119/shankar20c.html)).
+
+Even though the CLIP models trained with
+this codebase achieve much lower accuracy than those trained by OpenAI, our models still lie on the same
+trend of improved effective robustness (the purple line). Therefore, we can study what makes
+CLIP robust without requiring industrial-scale compute.
+
+For more information on effective robustness, please see:
+
+- [Recht et al., 2019](https://arxiv.org/abs/1902.10811).
+- [Taori et al., 2020](https://arxiv.org/abs/2007.00644).
+- [Miller et al., 2021](https://arxiv.org/abs/2107.04649).
+
+To know more about the factors that contribute to CLIP's robustness refer to [Fang et al., 2022](https://arxiv.org/abs/2205.01397).

Finetuning/docs/PRETRAINED.md

@@ -0,0 +1,288 @@
+## Pretrained model results
+
+We evaluate the full collection of available models on a suite of 38 datasets in a zero-shot setting (i.e., without fine-tuning), following [Gadre et al., 2023](https://arxiv.org/abs/2304.14108).
+Click below to see the full results.
+
+- [Full results (English)](openclip_results.csv)
+- [Classification-only results](openclip_classification_results.csv)
+- [Retrieval results](openclip_retrieval_results.csv)
+- [Multilingual retrieval results](openclip_multilingual_retrieval_results.csv)
+
+## Pretrained model details
+
+Below are details for several of our pretrained models.
+
+### LAION-400M - https://laion.ai/laion-400-open-dataset
+
+We ran experiments in an attempt to reproduce OpenAI's ViT results with the comparably sized (and open) LAION-400M dataset. Trained
+weights can be found in release [v0.2](https://github.com/mlfoundations/open_clip/releases/tag/v0.2-weights).
+
+The LAION400M weights have been trained on the JUWELS supercomputer (see acknowledgements section below).
+
+#### ViT-B/32 224x224
+
+We replicate OpenAI's results on ViT-B/32, reaching a top-1 ImageNet-1k zero-shot accuracy of 62.96%.
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot.png" width="700">
+
+**Zero-shot comparison (courtesy of Andreas Fürst)**
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_openai_compare_b32.jpg" width="700">
+
+ViT-B/32 was trained with 128 A100 (40 GB) GPUs for ~36 hours, 4600 GPU-hours. The per-GPU batch size was 256 for a global batch size of 32768. 256 is much lower than it could have been (~320-384) due to being sized initially before moving to 'local' contrastive loss.
+
+#### ViT-B/16 224x224
+
+The B/16 LAION400M training reached a top-1 ImageNet-1k zero-shot validation score of 67.07.
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_b16.png" width="700">
+
+This was the first major train session using the updated webdataset 0.2.x code. A bug was found that prevented shards from being shuffled properly between nodes/workers each epoch. This was fixed part way through training (epoch 26) but likely had an impact.
+
+ViT-B/16 was trained with 176 A100 (40 GB) GPUS for ~61 hours, 10700 GPU-hours. Batch size per GPU was 192 for a global batch size of 33792.
+
+#### ViT-B/16+ 240x240
+
+The B/16+ 240x240 LAION400M training reached a top-1 ImageNet-1k zero-shot validation score of 69.21.
+
+This model is the same depth as the B/16, but increases the
+
+- vision width from 768 -> 896
+- text width from 512 -> 640
+- the resolution 224x224 -> 240x240 (196 -> 225 tokens)
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_b16_plus_240.png" width="700">
+
+Unlike the B/16 run above, this model was a clean run with no dataset shuffling issues.
+
+ViT-B/16+ was trained with 224 A100 (40 GB) GPUS for ~61 hours, 13620 GPU-hours. Batch size per GPU was 160 for a global batch size of 35840.
+
+#### ViT-L/14 224x224
+
+The L/14 LAION-400M training reached a top-1 ImageNet-1k zero-shot validation score of 72.77.
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion_clip_zeroshot_l14.png" width="700">
+
+ViT-L/14 was trained with 400 A100 (40 GB) GPUS for ~127 hours, 50800 GPU-hours. Batch size per GPU was 96 for a global batch size of 38400. Grad checkpointing was enabled.
+
+### LAION-2B (en) - https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/
+
+A ~2B sample subset of LAION-5B with english captions (https://huggingface.co/datasets/laion/laion2B-en)
+
+#### ViT-B/32 224x224
+
+A ViT-B/32 trained on LAION-2B, reaching a top-1 ImageNet-1k zero-shot accuracy of 65.62%.
+
+<img src="https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/laion2b_clip_zeroshot_b32.png" width="700">
+
+ViT-B/32 was trained with 112 A100 (40 GB) GPUs. The per-GPU batch size was 416 for a global batch size of 46592. Compute generously provided by [stability.ai](https://stability.ai/).
+
+A second iteration of B/32 was trained on stability.ai cluster with a larger global batch size and learning rate, hitting 66.6% top-1. See https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K
+
+#### ViT-L/14 224x224
+
+A ViT-L/14 with a 75.3% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K
+
+These weights use a different dataset mean and std than others. Instead of using the OpenAI mean & std, inception style normalization `[-1, 1]` is used via a mean and std of `[0.5, 0.5, 0.5]`. This is handled automatically if using `open_clip.create_model_and_transforms` from pretrained weights.
+
+#### ViT-H/14 224x224
+
+A ViT-H/14 with a 78.0% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
+
+#### ViT-g/14 224x224
+
+A ViT-g/14 with a 76.6% top-1 ImageNet-1k zero-shot was trained on JUWELS Booster. See model details here https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K
+
+This model was trained with a shorted schedule than other LAION-2B models with 12B samples seen instead of 32+B. It matches LAION-400M training in samples seen. Many zero-shot results are lower as a result, but despite this it performs very well in some OOD zero-shot and retrieval tasks.
+
+#### ViT-B/32 roberta base
+
+A ViT-B/32 with roberta base encoder with a 61.7% top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k
+This is the first openclip model using a HF text tower. It has better performance on a range of tasks compared to the standard text encoder, see [metrics](https://huggingface.co/laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/blob/main/unknown.png)
+
+#### ViT-B/32 xlm roberta base
+
+A ViT-B/32 with xlm roberta base encoder with a 62.33% top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k
+This is the first openclip model trained on the full laion5B dataset; hence the first multilingual clip trained with openclip. It has better performance on a range of tasks compared to the standard text encoder, see [metrics](https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/blob/main/metrics.png)
+A preliminary multilingual evaluation was run: 43% on imagenet1k italian (vs 21% for english B/32), 37% for imagenet1k japanese (vs 1% for english B/32 and 50% for B/16 clip japanese). It shows the multilingual property is indeed there as expected. Larger models will get even better performance.
+
+#### ViT-H/14 xlm roberta large
+
+A ViT-H/14 with xlm roberta large encoder with a 77.0% (vs 78% for the english equivalent) top-1 ImageNet-1k zero-shot was trained on stability. See model details here https://huggingface.co/laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k
+
+This model was trained following the [LiT](https://arxiv.org/abs/2111.07991) methodology: the image tower was frozen (initialized from english openclip ViT-H/14), the text tower was initialized from [xlm roberta large](https://huggingface.co/xlm-roberta-large) and unfrozen. This reduced training cost by a 3x factor.
+
+See full english [metrics](https://huggingface.co/laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/resolve/main/results_xlm_roberta_large.png)
+
+On zero shot classification on imagenet with translated prompts this model reaches:
+
+- 56% in italian (vs 21% for https://github.com/clip-italian/clip-italian)
+- 53% in japanese (vs 54.6% for https://github.com/rinnakk/japanese-clip)
+- 55.7% in chinese (to be compared with https://github.com/OFA-Sys/Chinese-CLIP)
+
+#### YFCC-15M
+
+Below are checkpoints of models trained on YFCC-15M, along with their zero-shot top-1 accuracies on ImageNet and ImageNetV2. These models were trained using 8 GPUs and the same hyperparameters described in the "Sample running code" section, with the exception of `lr=5e-4` and `epochs=32`.
+
+- [ResNet-50](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt) (32.7% / 27.9%)
+- [ResNet-101](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt) (34.8% / 30.0%)
+
+#### CC12M - https://github.com/google-research-datasets/conceptual-12m
+
+- [ResNet-50](https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt) (36.45%)
+
+### CommonPool and DataComp models
+
+As part of [DataComp](https://github.com/mlfoundations/datacomp), we trained models on CommonPool using various data filtering strategies.
+
+The best performing models are specified below for the xlarge scale, see our paper [DataComp: In seearch of the next generation of multimodal datasets](https://arxiv.org/abs/2304.14108) for more details.
+
+Additional models and more information can be found at [/docs/datacomp_models.md](/docs/datacomp_models.md).
+
+- `datacomp_xl_s13b_b90k`: A ViT-L/14 trained on DataComp-1B for 12.8B steps and batch size 90k. Achieves 79.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K.
+
+- `commonpool_xl_clip_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using CLIP scores, for 12.8B steps and batch size 90k. Achieves 76.4% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K.
+
+- `commonpool_xl_laion_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using the LAION-2B filtering scheme, for 12.8B steps and batch size 90k. Achieves 75.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K.
+
+- `commonpool_xl_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL without any filtering, for 12.8B steps and batch size 90k. Achieves 72.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K.
+
+If you use models trained on DataComp-1B or CommonPool variations, please consider citing the following:
+
+```bibtex
+@article{datacomp,
+  title={DataComp: In search of the next generation of multimodal datasets},
+  author={Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt},
+  journal={arXiv preprint arXiv:2304.14108},
+  year={2023}
+}
+```
+
+### MetaCLIP
+
+MetaCLIP models are described in the paper [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671).
+These models were developed by Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer and Christoph Feichtenhofer from Meta, New York University and the University of Washington.
+
+Models are licensed under CC-BY-NC.
+More details are available at https://github.com/facebookresearch/MetaCLIP.
+
+If you use MetaCLIP models, please cite the following:
+
+```bibtex
+@inproceedings{xu2023metaclip,
+   title={Demystifying CLIP Data},
+   author={Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu, Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer and Christoph Feichtenhofer},
+   journal={arXiv preprint arXiv:2309.16671},
+   year={2023}
+}
+```
+
+### EVA-CLIP
+
+EVA-CLIP models are described in the paper [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://arxiv.org/abs/2303.15389).
+These models were developed by Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang and Yue Cao from BAAI and HUST.
+
+Models are licensed under the MIT License.
+More details are available at https://github.com/baaivision/EVA/tree/master/EVA-CLIP.
+
+If you use EVA models, please cite the following:
+
+```bibtex
+@article{EVA-CLIP,
+  title={EVA-CLIP: Improved Training Techniques for CLIP at Scale},
+  author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue},
+  journal={arXiv preprint arXiv:2303.15389},
+  year={2023}
+}
+```
+
+### NLLB-CLIP
+
+NLLB-CLIP models are described in the paper [NLLB-CLIP - train performant multilingual image retrieval model on a budget](https://arxiv.org/abs/2309.01859) by Alexander Visheratin.
+
+The model was trained following the [LiT](https://arxiv.org/abs/2111.07991) methodology: the image tower was frozen, the text tower was initialized from the [NLLB](https://arxiv.org/abs/2207.04672) encoder and unfrozen.
+
+The model was trained on the [LAION-COCO-NLLB](https://huggingface.co/datasets/visheratin/laion-coco-nllb) dataset.
+
+The first version of the model (`nllb-clip`) described in the paper was trained using the OpenAI CLIP image encoder.
+
+The second version of the model (`nllb-clip-siglip`) was trained using the [SigLIP](https://arxiv.org/abs/2303.15343) image encoder.
+
+Models are licensed under CC-BY-NC.
+
+If you use NLLB-CLIP models, please cite the following:
+
+```bibtex
+@article{visheratin2023nllb,
+  title={NLLB-CLIP--train performant multilingual image retrieval model on a budget},
+  author={Visheratin, Alexander},
+  journal={arXiv preprint arXiv:2309.01859},
+  year={2023}
+}
+```
+
+### CLIPA
+
+CLIPA models are described in the following papers by Xianhang Li, Zeyu Wang, Cihang Xie from UC Santa Cruz:
+
+- [An Inverse Scaling Law for CLIP Training](https://arxiv.org/abs/2305.07017)
+- [CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy](https://arxiv.org/abs/2306.15658)
+
+Models are licensed under Apache 2.0.
+More details are available at https://github.com/UCSC-VLAA/CLIPA and [here](clipa.md).
+
+If you use CLIPA models, please cite the following:
+
+```bibtex
+@inproceedings{li2023clipa,
+      title={An Inverse Scaling Law for CLIP Training},
+      author={Xianhang Li and Zeyu Wang and Cihang Xie},
+      booktitle={NeurIPS},
+      year={2023},
+}
+```
+
+```bibtex
+@article{li2023clipav2,
+      title={CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy},
+      author={Xianhang Li and Zeyu Wang and Cihang Xie},
+      journal={arXiv preprint arXiv:2306.15658},
+      year={2023},
+}
+```
+
+### SigLIP
+
+SigLIP models are described in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343).
+These models were developed by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer from Google Deepmind.
+
+Models are licensed under the Apache 2 license.
+More details are available at hhttps://github.com/google-research/big_vision.
+
+If you use SigLIP models, please cite the following:
+
+```bibtex
+@article{zhai2023sigmoid,
+  title={Sigmoid loss for language image pre-training},
+  author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
+  journal={arXiv preprint arXiv:2303.15343},
+  year={2023}
+}
+```
+
+### DFN
+
+Data Filtering Network models are described in https://arxiv.org/abs/2309.17425.
+These models were developed by Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev and Vaishaal Shankar from Apple and the University of Washington.
+
+Models are licensed under the following: https://huggingface.co/apple/DFN5B-CLIP-ViT-H-14-384/blob/main/LICENSE.
+
+If you use DFN models, please cite the following:
+
+```bibtext
+@article{fang2023data,
+  title={Data Filtering Networks},
+  author={Fang, Alex and Jose, Albin Madappally and Jain, Amit and Schmidt, Ludwig and Toshev, Alexander and Shankar, Vaishaal},
+  journal={arXiv preprint arXiv:2309.17425},
+  year={2023}
+}
+```

Finetuning/docs/clip_conceptual_captions.md

@@ -0,0 +1,13 @@
+## Additional training curves for CLIP on Conceptual Captions
+
+# Zero shot accuracy
+![](/docs/clip_zeroshot.png)
+
+# Training loss curve
+![](/docs/clip_loss.png)
+
+# Validation loss curve
+![](/docs/clip_val_loss.png)
+
+# Validation recall
+![](/docs/clip_recall.png)

Finetuning/docs/clipa.md

@@ -0,0 +1,103 @@
+## CLIPA
+
+In this work, we present a surprising finding that there exists an _inverse_ scaling law for CLIP training, 
+whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training. 
+Moreover, we showcase that the strategy for reducing image/text token length plays a crucial role in determining the quality of this scaling law.
+
+![](/docs/inverse_scaling_law.png)
+
+As a result of this finding, we are able to successfully train CLIP even by using academic resources. 
+For example, on an A100 eight-GPU server, our CLIP models achieve zero-shot top-1 ImageNet accuracies of **63.2%** in about **2 days**, 
+**67.8%** in about **3 days**, and **69.3%** in about **4 days**.
+
+Moreover, We find that CLIPA at scale leads to state-of-the-art performance. For example, our CLIPA-v2 H/14 achieves a zero-shot top-1 ImageNet accuracy of **81.8%**,
+with a budget less than **$15000**.
+
+![](/docs/clipa_acc_compute.png)
+
+For more details, please see our paper [An Inverse Scaling Law for CLIP Training](https://arxiv.org/abs/2305.07017) and 
+[CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy](https://arxiv.org/abs/2306.15658).
+
+
+Eight token length reduction strategies are investigated in this work, detailed as follows.
+
+
+## Image token length reduction
+
+![](/docs/clipa_reduce_image_token.png)
+
+* `resize`: use `--force-image-size` to specify the image size you want to adopt. We find this strategy generally works the best as it retains full image information.
+
+* `random mask`: Randomly mask out image patches. use `--force-patch-dropout` to specify the mask ratio you want to adopt. 
+
+* `grid mask`: Preserve one patch in each 2 × 2 grid window. We do not provide implementation for grid masking, as it is only experimental and we generally find resizing works better.
+
+* `block mask`: Keep a single block and remove other patches. We do not provide implementation for block masking, as it is only experimental and we generally find resizing works better.
+
+
+## Text token length reduction
+
+* `syntax mask`: Assign different masking priorities to parts of speech. Specify `"text_mask": syntax` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+Specifically, we prioritize retaining nouns, followed by adjectives, and then other words. 
+We find this strategy generally works the best as it retains critical information for contrastive learning.
+
+* `truncate`: Truncation selects the first N text tokens and discards the rest. This is the default setting of `open_clip`. 
+
+* `random mask`: Randomly drops a portion of the text tokens. Specify `"text_mask": random` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+
+* `block mask`: Randomly preserves consecutive text sequences. Specify `"text_mask": block` in `"tokenizer_kwargs"` in `"text_cfg"` of model config `json` file to use. 
+
+
+## Installation
+
+The installation is really the same as `open_clip`, except for the usage of Natural Language Toolkit (NLTK) in `syntax mask` of text token length reduction.
+Please follow the [official doc](https://www.nltk.org/) to install NLTK.
+
+Note that the the usage of NLTK brings two constraints:
+* Because certain functions like `nltk.pos_tag` from NLTK only support English and Russian for now, the `syntax mask` only works for English. 
+we have not tested it on Russian or any other language. Theoretically, it should work the same, given a proper language processing toolkit for other languages.
+If you still want to apply `syntax mask` on other languages, try finding the right toolkit. Otherwise, use other text token length reduction strategies
+* some modules of NLTK like `punkt` or `averaged_perceptron_tagger` need to be downloaded first before using NLTK.
+We have included the downloading code in `tokenizer.py`, but this might cause trouble in certain cases.
+You may want to manually download those modules first, by `nltk.download('punkt')` and `nltk.download('averaged_perceptron_tagger')`,
+and then setup the environmental variable before running the script `export NLTK_DATA=cache`. 
+Note that this is a one-time effort. Remember to comment out those `nltk.download` lines in `tokenizer.py` afterwards.
+
+## Training
+We provide example scripts to reproduce our CLIPA results on an A100 eight-GPU machine under path `docs/script_examples/clipa`.
+
+For instance, to reproduce the CLIPA-L16(I37,T8) results, first run the pre-training script
+```
+bash docs/script_examples/clipa/vit_l16/i37_t8_pretrain.sh
+```
+and fine-tune the pre-trained checkpoint with
+```
+bash docs/script_examples/clipa/vit_l16/i37_t8_finetune.sh
+```
+- Remember to change the path to dataset to your own path.
+- This is a two-stage training pipeline. Remember to change the path to pre-trained checkpoint to your own when fine-tuning.
+- The training time is ~3 days for pre-training and ~1 day for fine-tuning on an A100 eight-GPU machine.
+
+## Model Weights
+Below are CLIPA trained weights on LAION-400M with an A100 eight-GPU machine. 
+All models are pre-trained for 6 epochs with reduced input token lengths and subsequently fine-tuned for 0.36 epoch with full input token lengths.
+
+
+|                     |                                      Pre-trained Weights                                       | zero-shot IN-1K |
+|---------------------|:----------------------------------------------------------------------------------------------:|:---------------:|
+| CLIPA-B/16(I50,T16) | [download](https://drive.google.com/file/d/1MDpz8gV2Vjaazk16rBhLxU8811U7_cGL/view?usp=sharing) |      59.7       |
+| CLIPA-L/16(I17,T16) | [download](https://drive.google.com/file/d/1Tr2GYiKAaMH6EGIn5l7eX_1K20eaA3WA/view?usp=sharing) |      60.3       |
+| CLIPA_L/16(I37,T8)  | [download](https://drive.google.com/file/d/1EM1ChRNARpLckkJjf6m7njCY3xyvpGBu/view?usp=sharing) |      57.9       |
+
+|                     |                                       Fine-tuned Weights                                       | zero-shot IN-1K |
+|---------------------|:----------------------------------------------------------------------------------------------:|:-----:|
+| CLIPA-B/16(I50,T16) | [download](https://drive.google.com/file/d/1fURK0K_a3-83jVEI4PVEbnEJb_V6UbGv/view?usp=sharing) | 63.2  |
+| CLIPA-L/16(I17,T16) | [download](https://drive.google.com/file/d/18qqZGOTGOgb3I3JWONuat6qObsgLq7sR/view?usp=sharing) | 67.8  |
+| CLIPA_L/16(I37,T8)  | [download](https://drive.google.com/file/d/1lV7pLORUK04T9QKKx9TpYtMws-AZrib0/view?usp=sharing) | 69.3  |
+
+
+## CLIPA-v2
+We also provide example scripts to reproduce our CLIPA-v2 H/14 results under path `docs/script_examples/clipav2`.
+Note that the original results are obtained with [our JAX implementation](https://github.com/UCSC-VLAA/CLIPA/tree/master/clipa_jax).
+These scripts are written after manually scanning the JAX config files.
+As it is infeasible for us to retrain those models again with pytorch, its correctness cannot be verified with 100% confidence. Use them at your own discretion.

Finetuning/docs/datacomp_models.md

@@ -0,0 +1,69 @@
+## CommonPool and DataComp models
+
+As part of [DataComp](https://github.com/mlfoundations/datacomp), we trained models on CommonPool using various data filtering strategies.
+We release models for all four scales of the competition, small, medium, large and xlarge, corresponding to a pool size and number of samples seen of 12.8M, 128M, 1.28B and 12.8B, respectively.
+
+The models are specified below, see our paper [DataComp: In seearch of the next generation of multimodal datasets](https://arxiv.org/abs/2304.14108) for more details.
+
+
+## xlarge scale models
+
+* `datacomp_xl_s13b_b90k`: A ViT-L/14 trained on DataComp-1B for 12.8B steps and batch size 90k. Achieves 79.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K. 
+
+* `commonpool_xl_clip_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using CLIP scores, for 12.8B steps and batch size 90k. Achieves 76.4% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K.
+
+* `commonpool_xl_laion_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL filtered using the LAION-2B filtering scheme, for 12.8B steps and batch size 90k. Achieves 75.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K.
+
+* `commonpool_xl_s13b_b90k`: A ViT-L/14 trained on CommonPool-XL without any filtering, for 12.8B steps and batch size 90k. Achieves 72.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K.
+
+
+## large scale models
+
+* `datacomp_l_s1b_b8k`: A ViT-B/16 trained on a 140M subset of DataComp-1B, for 1.28B steps and batch size 8k. Achieves 63.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-DataComp.L-s1B-b8K.
+
+* `commonpool_l_clip_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using CLIP scores, for 1.28B steps and batch size 8k. Achieves 57.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.clip-s1B-b8K.
+
+* `commonpool_l_laion_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using the LAION-2B filtering scheme, for 1.28B steps and batch size 8k. Achieves 55.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.laion-s1B-b8K.
+
+* `commonpool_l_image_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using image-based filtering, for 1.28B steps and batch size 8k. Achieves 57.2% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.image-s1B-b8K.
+
+* `commonpool_l_text_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using text-based filtering, for 1.28B steps and batch size 8k. Achieves 56.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.text-s1B-b8K.
+
+* `commonpool_l_basic_s1b_b8k`: A ViT-B/16 trained on CommonPool-L filtered using basic filtering (English filtering + caption length and image size filtering), for 1.28B steps and batch size 8k. Achieves 51.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L.basic-s1B-b8K.
+
+* `commonpool_l_s1b_b8k`: A ViT-B/16 trained on CommonPool-L without any filtering, for 1.28B steps and batch size 8k. Achieves 45.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-16-CommonPool.L-s1B-b8K.
+
+
+## medium scale models
+
+* `datacomp_m_s128m_b4k`: A ViT-B/32 trained on a 14M subset of DataComp-1B, for 128M steps and batch size 4k. Achieves 29.7% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.M-s128M-b4K.
+
+* `commonpool_m_clip_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using CLIP scores, for 128M steps and batch size 4k. Achieves 27.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.clip-s128M-b4K.
+
+* `commonpool_m_laion_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using the LAION-2B filtering scheme, for 128M steps and batch size 4k. Achieves 23.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.laion-s128M-b4K.
+
+* `commonpool_m_image_s128m_b4k`: A ViT-B/32 trained on CommonPool-M filtered using image-based filtering, for 128M steps and batch size 4k. Achieves 26.8% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.image-s128M-b4K.
+
+* `commonpool_m_text_s128m_b4k`:  A ViT-B/32 trained on CommonPool-M filtered using text-based filtering, for 128M steps and batch size 4k. Achieves 25.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.text-s128M-b4K.
+
+* `commonpool_m_basic_s128m_b4k`:  A ViT-B/32 trained on CommonPool-M filtered using basic filtering (English filtering + caption length and image size filtering), for 128M steps and batch size 4k. Achieves 22.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M.basic-s128M-b4K.
+
+* `commonpool_m_s128m_b4k`: A ViT-B/32 trained on CommonPool-M without any filtering, for 128M steps and batch size 4k. Achieves 17.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.M-s128M-b4K.
+
+
+## small scale models
+
+* `datacomp_s_s13m_b4k`: A ViT-B/32 trained on a 1.4M subset of DataComp-1B, for 12.8M steps and batch size 4k. Achieves 3.9% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-DataComp.S-s13M-b4K.
+
+* `commonpool_s_clip_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using CLIP scores, for 12.8M steps and batch size 4k. Achieves 5.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.clip-s13M-b4K.
+
+* `commonpool_s_laion_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using the LAION-2B filtering scheme scores, for 12.8M steps and batch size 4k. Achieves 3.1% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.laion-s13M-b4K.
+
+* `commonpool_s_image_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using image-based filtering, for 12.8M steps and batch size 4k. Achieves 4.3% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.image-s13M-b4K.
+
+* `commonpool_s_text_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using text-based filtering, for 12.8M steps and batch size 4k. Achieves 4.6% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.text-s13M-b4K.
+
+* `commonpool_s_basic_s13m_b4k`: A ViT-B/32 trained on CommonPool-S filtered using basic filtering (English filtering + caption length and image size filtering), for 12.8M steps and batch size 4k. Achieves 3.0% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S.basic-s13M-b4K.
+
+* `commonpool_s_s13m_b4k`: A ViT-B/32 trained on CommonPool-S without any filtering, for 12.8M steps and batch size 4k. Achieves 2.5% zero-shot accuracy on ImageNet. Available at https://huggingface.co/laion/CLIP-ViT-B-32-CommonPool.S-s13M-b4K.
+

Finetuning/docs/model_profile.csv

@@ -0,0 +1,86 @@
+model,image_size,image_width,text_width,embed_dim,mparams,image_mparams,text_mparams,gflops,image_gflops,text_gflops
+ViT-S-32-alt,224,384,256,256,43.22,22.59,20.63,3.56,2.29,1.27
+ViT-S-32,224,384,384,384,63.09,22.64,40.44,5.66,2.29,3.38
+ViT-M-32-alt,224,512,384,384,80.07,39.63,40.44,7.37,3.99,3.38
+ViT-M-32,224,512,512,512,103.12,39.69,63.43,9.95,3.99,5.96
+ViT-S-16-alt,224,384,256,256,42.4,21.76,20.63,10.47,9.2,1.27
+ViT-S-16,224,384,384,384,62.26,21.81,40.44,12.58,9.2,3.38
+ViT-B-32,224,768,512,512,151.28,87.85,63.43,14.78,8.82,5.96
+ViT-B-32-quickgelu,224,768,512,512,151.28,87.85,63.43,14.78,8.82,5.96
+convnext_tiny,224,768,512,1024,92.3,28.61,63.69,14.87,8.91,5.96
+ViT-B-32-256,256,768,512,512,151.29,87.86,63.43,17.46,11.5,5.96
+RN50,224,64,512,1024,102.01,38.32,63.69,18.18,12.22,5.96
+RN50-quickgelu,224,64,512,1024,102.01,38.32,63.69,18.18,12.22,5.96
+ViT-M-16-alt,224,512,384,384,78.98,38.53,40.44,19.36,15.98,3.38
+ViT-M-16,224,512,512,512,102.02,38.59,63.43,21.94,15.98,5.96
+vit_relpos_medium_patch16_cls_224,224,768,512,512,101.94,38.51,63.43,21.99,16.03,5.96
+mt5-base-ViT-B-32,224,768,512,512,365.71,87.85,277.86,22.12,8.82,13.3
+convnext_small,224,768,512,512,113.28,49.85,63.43,23.33,17.37,5.96
+ViT-B-32-plus-256,256,896,640,640,210.3,119.13,91.16,24.83,15.56,9.27
+RN101,224,64,512,512,119.69,56.26,63.43,25.5,19.54,5.96
+RN101-quickgelu,224,64,512,512,119.69,56.26,63.43,25.5,19.54,5.96
+vit_medium_patch16_gap_256,256,768,512,512,102.04,38.61,63.43,27.1,21.14,5.96
+coca_ViT-B-32,224,768,512,512,253.56,89.16,63.43,33.34,9.19,5.96
+convnext_base,224,768,512,512,151.52,88.09,63.43,36.67,30.71,5.96
+swin_base_patch4_window7_224,224,768,640,640,178.56,87.4,91.16,40.13,30.86,9.27
+ViT-B-16,224,768,512,512,149.62,86.19,63.43,41.09,35.13,5.96
+ViT-B-16-quickgelu,224,768,512,512,149.62,86.19,63.43,41.09,35.13,5.96
+EVA02-B-16,224,768,512,512,149.69,86.26,63.43,41.09,35.13,5.96
+ViT-B-16-SigLIP,224,768,768,768,203.16,92.88,110.27,46.44,35.42,11.02
+convnext_base_w,256,768,640,640,179.39,88.22,91.16,49.38,40.11,9.27
+RN50x4,288,80,640,640,178.3,87.14,91.16,51.82,42.56,9.27
+coca_roberta-ViT-B-32,224,768,768,512,420.37,87.85,124.45,53.12,8.82,13.12
+ViT-B-16-plus,224,896,640,640,208.35,117.19,91.16,56.75,47.49,9.27
+ViT-B-16-SigLIP-256,256,768,768,768,203.2,92.93,110.27,57.84,46.82,11.02
+ViT-B-16-SigLIP-i18n-256,256,768,768,768,370.63,92.93,277.7,57.84,46.82,11.02
+ViT-B-16-plus-240,240,896,640,640,208.38,117.21,91.16,64.03,54.76,9.27
+convnext_base_w_320,320,768,640,640,179.39,88.22,91.16,71.94,62.67,9.27
+convnext_large,224,768,768,768,321.06,197.41,123.65,82.02,68.72,13.3
+coca_base,288,768,768,512,440.34,86.4,134.66,99.09,46.47,13.3
+roberta-ViT-B-32,224,768,512,512,212.72,87.85,124.87,105.87,8.82,97.05
+xlm-roberta-base-ViT-B-32,224,768,512,512,366.12,87.85,278.27,105.87,8.82,97.05
+convnext_large_d,256,768,768,768,351.77,199.77,152.0,107.5,89.76,17.73
+ViT-B-16-SigLIP-384,384,768,768,768,203.45,93.18,110.27,123.15,112.13,11.02
+ViT-L-16,224,1024,768,768,427.74,304.09,123.65,136.41,123.11,13.3
+convnext_large_d_320,320,768,768,768,351.77,199.77,152.0,157.98,140.25,17.73
+RN50x16,384,96,768,768,290.98,167.33,123.65,162.69,149.39,13.3
+ViT-L-14-CLIPA,224,1024,768,768,414.21,303.96,110.25,167.5,162.03,5.47
+EVA02-L-14,224,768,768,768,427.76,304.11,123.65,175.3,162.0,13.3
+ViT-L-14,224,1024,768,768,427.62,303.97,123.65,175.33,162.03,13.3
+ViT-L-14-quickgelu,224,1024,768,768,427.62,303.97,123.65,175.33,162.03,13.3
+convnext_xlarge,256,768,1024,1024,653.89,350.25,303.65,198.38,159.14,39.24
+ViT-L-16-SigLIP-256,256,768,1024,1024,652.15,315.96,336.19,201.62,162.56,39.06
+coca_ViT-L-14,224,1024,768,768,638.45,306.72,123.65,214.52,163.64,13.3
+ViT-B-16-SigLIP-512,512,768,768,768,203.79,93.52,110.27,227.26,216.24,11.02
+ViT-SO400M-14-SigLIP,224,768,1152,1152,877.36,427.68,449.68,233.54,220.35,13.19
+ViT-L-14-280,280,1024,768,768,427.76,304.11,123.65,271.79,258.49,13.3
+ViT-L-16-320,320,1024,768,768,427.95,304.3,123.65,271.93,258.63,13.3
+ViT-H-16,224,1280,1024,1024,986.26,632.23,354.03,301.72,254.63,47.09
+ViT-H-14-CLIPA,224,1280,1024,1024,968.24,632.07,336.16,354.02,334.59,19.43
+nllb-clip-base,224,768,512,512,501.89,87.85,414.04,369.6,8.82,360.78
+ViT-H-14,224,1280,1024,1024,986.11,632.08,354.03,381.68,334.59,47.09
+ViT-H-14-quickgelu,224,1280,1024,1024,986.11,632.08,354.03,381.68,334.59,47.09
+ViT-L-14-CLIPA-336,336,1024,768,768,414.54,304.29,110.25,387.39,381.92,5.47
+EVA02-L-14-336,336,768,768,768,428.08,304.43,123.65,395.16,381.86,13.3
+ViT-L-14-336,336,1024,768,768,427.94,304.29,123.65,395.22,381.92,13.3
+ViT-L-16-SigLIP-384,384,768,1024,1024,652.48,316.28,336.19,422.91,383.85,39.06
+convnext_xxlarge,256,768,1024,1024,1200.58,846.54,354.03,443.03,395.94,47.09
+nllb-clip-base-siglip,384,768,512,768,507.47,93.18,414.3,472.91,112.13,360.78
+mt5-xl-ViT-H-14,224,1280,512,1024,2306.75,632.08,1674.68,514.04,334.59,179.45
+EVA01-g-14,224,768,768,1024,1136.44,1012.59,123.85,547.36,534.06,13.3
+RN50x64,448,128,1024,1024,623.26,420.38,202.88,552.65,529.11,23.55
+EVA01-g-14-plus,224,768,1024,1024,1366.62,1012.59,354.03,581.15,534.06,47.09
+ViT-g-14,224,1408,1024,1024,1366.68,1012.65,354.03,581.15,534.06,47.09
+convnext_xxlarge_320,320,768,1024,1024,1200.58,846.54,354.03,665.74,618.65,47.09
+xlm-roberta-large-ViT-H-14,224,1280,512,1024,1193.01,632.08,560.94,671.01,334.59,336.42
+ViT-SO400M-14-SigLIP-384,384,768,1152,1152,877.96,428.23,449.73,723.48,670.35,53.13
+ViT-H-14-CLIPA-336,336,1280,1024,1024,968.64,632.48,336.16,800.88,781.45,19.43
+ViT-bigG-14-CLIPA,224,1664,1280,1280,2517.22,1844.9,672.32,1007.93,967.5,40.44
+ViT-H-14-378-quickgelu,378,1280,1024,1024,986.71,632.68,354.03,1054.05,1006.96,47.09
+ViT-bigG-14,224,1664,1280,1280,2539.57,1844.91,694.66,1065.36,967.5,97.86
+nllb-clip-large,224,1280,512,1024,1399.22,632.08,767.14,1468.46,334.59,1133.87
+nllb-clip-large-siglip,384,768,512,1152,1195.5,428.23,767.27,1804.22,670.35,1133.87
+ViT-e-14,224,1792,1280,1280,4581.09,3807.72,773.37,2091.45,1981.35,110.1
+ViT-bigG-14-CLIPA-336,336,1664,1280,1280,2517.76,1845.44,672.32,2271.58,2231.15,40.44
+EVA02-E-14,224,768,1024,1024,4704.59,4350.56,354.03,2311.42,2264.33,47.09
+EVA02-E-14-plus,224,768,1280,1024,5044.89,4350.56,694.33,2362.19,2264.33,97.86

Finetuning/docs/openclip_classification_results.csv

@@ -0,0 +1,122 @@
+name,pretrained,params (M),FLOPs (B),Average perf. on 35 datasets,ImageNet 1k,Caltech-101,CIFAR-10,CIFAR-100,CLEVR Counts,CLEVR Distance,Country211,Describable Textures,EuroSAT,FGVC Aircraft,Food-101,GTSRB,ImageNet Sketch,ImageNet v2,ImageNet-A,ImageNet-O,ImageNet-R,KITTI Vehicle Distance,MNIST,ObjectNet,Oxford Flowers-102,Oxford-IIIT Pet,Pascal VOC 2007,PatchCamelyon,Rendered SST2,RESISC45,Stanford Cars,STL-10,SUN397,SVHN,iWildCam,Camelyon17,FMoW,Dollar Street,GeoDE
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+ViT-B-32,datacomp_xl_s13b_b90k,151.28,14.78,0.5831,0.6917,0.9230,0.9561,0.8031,0.1294,0.2423,0.1756,0.5713,0.5746,0.2463,0.8632,0.5185,0.5676,0.6075,0.3035,0.4975,0.7818,0.1632,0.8124,0.5510,0.7353,0.9002,0.8151,0.5284,0.4849,0.6343,0.8728,0.9654,0.6780,0.6240,0.0863,0.6656,0.0000,0.5643,0.8731
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