Lukejacob Nsfw Image Detector

--- license: apache-2.0 base_model: - LukeJacob2023/nsfw-image-detector tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: nsfw-image-detector results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9315615772103526 library_name: transformers.js pipeline_tag: image-classification --- # nsfw-image-detector (ONNX) This is an ONNX version of [LukeJacob2023/nsfw-image-detector](https://huggingface.co/LukeJacob2023/nsfw-image-detector). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx). ## Usage with Transformers.js See the pipeline documentation for `image-classification`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageClassificationPipeline --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nsfw-image-detector This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8138 - Accuracy: 0.9316 - Accuracy K: 0.9887 You can access 384 version on: https://huggingface.co/LukeJacob2023/nsfw-image-detector-384 ## Model description Labels: ['drawings', 'hentai', 'neutral', 'porn', 'sexy'] ## Intended uses & limitations ## Training and evaluation data A custom dataset about 28k images, if you need to improve your domain's accurate, you can contribute the dataset to me. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy K | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| | 0.7836 | 1.0 | 720 | 0....