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vit-cats-dogs

KIRANKALLA

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Image classification
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Image classification
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resnet10

lerobot

It's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. - **Original Framework:** JAX - **Target Framework:** PyTorch - **Model Architecture:** ResNet-10 (4-stage ResNet with basic blocks) - **Original Model:** HIL-SERL ResNet-10 - **Total Parameters:** 4,905,792 (~4.9M parameters) - **Hidden Sizes:** [64, 128, 256, 512] - **Input:** 3-channel RGB images (128x128) - **Embedding Size:** 64

Image classification
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MobileNet-v2

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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NIMA-LiteRT

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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resnet18

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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resnet50

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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Plant leaf disease classifier for the capstone project **AI-Powered Plant Disease Detection and Farmer Assistance** (IIIT-H, Group 4). A farmer uploads a photo of a crop leaf; this model returns the most likely PlantVillage class and a confidence score. A LangGraph agent then explains the result and suggests treatment from a curated agricultural knowledge base. Confusion matrix: `reports/confusion_matrix.png` · Full per-class report: `reports/classification_report.txt` Coverage is the share of test images with top-1 probability at or above the threshold; the remaining images would be answered with "not sure". Recommended `CONFIDENCE_THRESHOLD` for the API: **0.5** (lowest threshold whose accuracy on covered images is at least 97% with coverage of at least 50%).

Image classification
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nsfw-image-detector

LukeJacob2023

This model is a fine-tuned version of 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 A custom dataset about 28k images, if you need to improve your domain's accurate, you can contribute the dataset to me. 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

Image classification
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