# Big Transfer (BiT) **Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet. ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('resnetv2_101x1_bitm', pretrained=True) model.eval() ``` To load and preprocess the image: ```python import urllib from PIL import Image from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform config = resolve_data_config({}, model=model) transform = create_transform(**config) url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") urllib.request.urlretrieve(url, filename) img = Image.open(filename).convert('RGB') tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```python import torch with torch.no_grad(): out = model(tensor) probabilities = torch.nn.functional.softmax(out[0], dim=0) print(probabilities.shape) # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```python # Get imagenet class mappings url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") urllib.request.urlretrieve(url, filename) with open("imagenet_classes.txt", "r") as f: categories = [s.strip() for s in f.readlines()] # Print top categories per image top5_prob, top5_catid = torch.topk(probabilities, 5) for i in range(top5_prob.size(0)): print(categories[top5_catid[i]], top5_prob[i].item()) # prints class names and probabilities like: # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `resnetv2_101x1_bitm`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```python model = timm.create_model('resnetv2_101x1_bitm', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc{kolesnikov2020big, title={Big Transfer (BiT): General Visual Representation Learning}, author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby}, year={2020}, eprint={1912.11370}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: Big Transfer Paper: Title: 'Big Transfer (BiT): General Visual Representation Learning' URL: https://paperswithcode.com/paper/large-scale-learning-of-general-visual Models: - Name: resnetv2_101x1_bitm In Collection: Big Transfer Metadata: FLOPs: 5330896 Parameters: 44540000 File Size: 178256468 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: Cloud TPUv3-512 ID: resnetv2_101x1_bitm LR: 0.03 Epochs: 90 Layers: 101 Crop Pct: '1.0' Momentum: 0.9 Batch Size: 4096 Image Size: '480' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L444 Weights: https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 82.21% Top 5 Accuracy: 96.47% - Name: resnetv2_101x3_bitm In Collection: Big Transfer Metadata: FLOPs: 15988688 Parameters: 387930000 File Size: 1551830100 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: Cloud TPUv3-512 ID: resnetv2_101x3_bitm LR: 0.03 Epochs: 90 Layers: 101 Crop Pct: '1.0' Momentum: 0.9 Batch Size: 4096 Image Size: '480' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L451 Weights: https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.38% Top 5 Accuracy: 97.37% - Name: resnetv2_152x2_bitm In Collection: Big Transfer Metadata: FLOPs: 10659792 Parameters: 236340000 File Size: 945476668 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M ID: resnetv2_152x2_bitm Crop Pct: '1.0' Image Size: '480' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L458 Weights: https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.4% Top 5 Accuracy: 97.43% - Name: resnetv2_152x4_bitm In Collection: Big Transfer Metadata: FLOPs: 21317584 Parameters: 936530000 File Size: 3746270104 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: Cloud TPUv3-512 ID: resnetv2_152x4_bitm Crop Pct: '1.0' Image Size: '480' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L465 Weights: https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.95% Top 5 Accuracy: 97.45% - Name: resnetv2_50x1_bitm In Collection: Big Transfer Metadata: FLOPs: 5330896 Parameters: 25550000 File Size: 102242668 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: Cloud TPUv3-512 ID: resnetv2_50x1_bitm LR: 0.03 Epochs: 90 Layers: 50 Crop Pct: '1.0' Momentum: 0.9 Batch Size: 4096 Image Size: '480' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L430 Weights: https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.19% Top 5 Accuracy: 95.63% - Name: resnetv2_50x3_bitm In Collection: Big Transfer Metadata: FLOPs: 15988688 Parameters: 217320000 File Size: 869321580 Architecture: - 1x1 Convolution - Bottleneck Residual Block - Convolution - Global Average Pooling - Group Normalization - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax - Weight Standardization Tasks: - Image Classification Training Techniques: - Mixup - SGD with Momentum - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: Cloud TPUv3-512 ID: resnetv2_50x3_bitm LR: 0.03 Epochs: 90 Layers: 50 Crop Pct: '1.0' Momentum: 0.9 Batch Size: 4096 Image Size: '480' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L437 Weights: https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 83.75% Top 5 Accuracy: 97.12% -->