collection curated by joecohen https://academictorrents.com/collection/imagenet @article{, title= {ImageNet-ValidationSet.zip}, journal= {}, author= {Keke Tang}, year= {}, url= {}, abstract= {}, keywords= {ImageNet 2012 Validation Set with Images Sorted with Categories}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/16c5dd6a172ac59e0f27d4b698e5399ea9d48160 @article{ilsvrc15, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, title= {ImageNet Large Scale Visual Recognition Challenge (V2017)}, year= {2015}, journal= {International Journal of Computer Vision (IJCV)}, doi= {10.1007/s11263-015-0816-y}, volume= {115}, number= {3}, pages= {211-252}, abstract= {}, keywords= {ILSVRC2017, ILSVRC, ImageNet, MLPerf}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/943977d8c96892d24237638335e481f3ccd54cfb @article{, title= {Downsampled ImageNet 32x32}, keywords= {}, author= {Aaron van den Oord and Nal Kalchbrenner and Koray Kavukcuoglu}, abstract= {This page includes downsampled ImageNet images, which can be used for density estimation and generative modeling experiments. Images come in two resolutions: 32x32 and 64x64, and were introduced in Pixel Recurrent Neural Networks. Please refer to the Pixel RNN paper for more details and results. }, terms= {}, license= {}, superseded= {}, url= {http://image-net.org/small/download.php} } https://academictorrents.com/download/bf62f5051ef878b9c357e6221e879629a9b4b172 @article{, title= {Downsampled ImageNet 64x64}, keywords= {}, author= {Aaron van den Oord and Nal Kalchbrenner and Koray Kavukcuoglu}, abstract= {This page includes downsampled ImageNet images, which can be used for density estimation and generative modeling experiments. Images come in two resolutions: 32x32 and 64x64, and were introduced in Pixel Recurrent Neural Networks. Please refer to the Pixel RNN paper for more details and results. }, terms= {}, license= {}, superseded= {}, url= {http://image-net.org/small/download.php} } https://academictorrents.com/download/96816a530ee002254d29bf7a61c0c158d3dedc3b @article{, title= {ImageNet LSVRC 2012 Validation Set (Object Detection)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2012}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5 @article{, title= {ImageNet LSVRC 2012 Training Set (Bounding Boxes)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2012}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/28202f4f8dde5c9b26d406f5522f8763713e605b @article{, title= {ImageNet LSVRC 2012 Validation Set (Bounding Boxes)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2012}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/dfa9ab2528ce76b907047aa8cf8fc792852facb9 @article{, title= {ImageNet LSVRC 2012 Training Set (Object Detection)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2012}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/a306397ccf9c2ead27155983c254227c0fd938e2 @article{, title= {Imagenet Full (Fall 2011 release)}, keywords= {imagenet, deep learning}, journal= {}, author= {Jia Deng and Wei Dong and Richard Socher and Li-Jia Li and Kai Li and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. For more information, see http://www.image-net.org/about-overview}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/564a77c1e1119da199ff32622a1609431b9f1c47 @article{, title= {ImageNet LSVRC 2013 Validation Set (Object Detection)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2015/#det}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/f47c081054f6301d908b5840bed507b3d981e669 @article{, title= {ImageNet LSVRC 2014 Training Set (Object Detection)}, keywords= {imagenet, deep learning}, journal= {}, author= {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, year= {}, url= {}, license= {}, abstract= {See http://image-net.org/challenges/LSVRC/2015/#det}, tos= {}, superseded= {}, terms= {You have been granted access for non-commercial research/educational use. By accessing the data, you have agreed to the following terms. You (the "Researcher") have requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.} } https://academictorrents.com/download/fbc7a9f9a10be134a1738ba947efa1814ed3ce9b