CLIP ViT L 14 laion2B s32B b82K

---
license: mit
widget:

• src: >-

https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
library_name: open_clip
pipeline_tag: zero-shot-image-classification
---

Model Card for CLIP ViT-L/14 - LAION-2B

Table of Contents

1. Model Details
2. Uses
3. Training Details
4. Evaluation
5. Acknowledgements
6. Citation
7. How To Get Started With the Model

Model Details

Model Description

A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).

Model training ('babysitting') done by Ross Wightman on the JUWELS Booster supercomputer. See acknowledgements below.

Uses

As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.

The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset.

Direct Use

Zero-shot image classification, image and text retrieval, among others.

Downstream Use

Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.

Out-of-Scope Use

As per the OpenAI models,

Any deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.

Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.

Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.

Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.

Training Details

Training Data

This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).

IMPORTANT NOTE: The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.

Training Procedure

The model was trained on 384 A100 GPUs using 200M sample 'virtual' epochs where dataset shards were sampled with replacement. The model was trained with 160 virtual epochs for a total of 32B samples seen.

The first 68 epochs were trained with float16 AMP, global batch size 79K (208 per GPU). Initially running to epoch 75, where the loss spiked and training failed with NaN.

Romain Beaumont was training H/14 and g/14 models at the same time on Stability cluster and hit similar instabilities. Collectively we tried restarts with,

• different dataset shuffle seed

• different LR

• gradient clipping

• modifications to the architecture

* Norm modifications (stable norm for final, post embed norm for text transformer) as per https://github.com/mlfoundations/open_clip/pull/153 thanks to Phil Wang
* Extra attention block norms ala Normformer (https://arxiv.org/abs/2110.09456)
* Scaled cosine attention ala Swin-V2 (https://arxiv.org/abs/2111.09883)

None of the above ended up working. Most blew up within the same epoch as original, with the exception of architecture mods.
* Normformer mods signifcantly altered the network such that resuming did not quickly converge to previous performance, this was abandoned but might be worth trying from start.
* Scaled cosine attn initially looked promising and lasted until epoch 90 before loss suddenly increased and appeared to remain 'stuck'.

In the end, restarting at epoch 69 with float32 precision solved all instabilities and training continued from there with global batch size 86k (224 per GPU). On A100 GPUs, float32 had a minimal impact on the throughput once tf32 matmuls were enabled in PyTorch. Approximately 10% slower than float16 AMP. Romain similary changed the precision but ended up using bfloat16 AMP to resolve issues.

Slum Script

```
#SBATCH --nodes=96
#SBATCH --gres=gpu:4
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=6
#SBATCH --wait-all-nodes=1
#SBATCH --job-name=open_clip_laion2b

load low-level libraries

ml purge source /conda/bin/activate pytorch-112

export NCCL_ASYNC_ERROR_HANDLING=1
export CUDA_VISIBLE_DEVICES=0,1,2,3
export MASTER_PORT=12802

get the first node name as master address - customized for vgg slurm

e.g. master(gnodee[2-5],gnoded1) == gnodee2

echo "NODELIST="${SLURM_NODELIST} master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) export MASTER_ADDR=$master_addr"i" echo "MASTER_ADDR="$MASTER_ADDR

cd /home/me/open_clip
export PYTHONPATH="$PYTHONPATH:$PWD/src"

srun --cpu_bind=none,v --accel-bind=gn python -u src/training/main.py \
--save-frequency 1 \
--zeroshot-frequency 1 \
--train-data="/data/laion2B-en/{00000..23295}.tar" \
--train-num-samples=200000000 \
--warmup 10000 \
--lr "1e-3" \
--batch-size=224 \
--epochs=160 \
--workers=6 \
--model ViT-L-14 \
--name "L14-laion2B" \
--report-to "tensorboard" \
--seed 0 \
--precision 'fp32' \
--ddp-static-graph \