Merge in upstream fixes & additions

HISTORY.md

@@ -1,3 +1,20 @@
+## 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

docs/LOW_ACC.md

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+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).

docs/PRETRAINED.md

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+## 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](docs/openclip_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.
+
+